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SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models | https://papers.nips.cc/paper_files/paper/2023/hash/0ff30c4bf31db0119a6219e0d250e037-Abstract-Conference.html | Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, ZHAO-XIANG ZHANG | https://papers.nips.cc/paper_files/paper/2023/hash/0ff30c4bf31db0119a6219e0d250e037-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19798-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0ff30c4bf31db0119a6219e0d250e037-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0ff30c4bf31db0119a6219e0d250e037-Supplemental-Conference.zip | Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests become a reachable goal. In this work, we propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We propose a set of atomic actions as an abstraction of spreadsheet software functionalities. We further design a state machine-based task planning framework for LLMs to robustly interact with spreadsheets. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline for rigorously benchmarking the ability of LLMs in software control tasks. Our SheetCopilot correctly completes 44.3\% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin. Our project page: https://sheetcopilot.github.io/. | null |
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets | https://papers.nips.cc/paper_files/paper/2023/hash/0ff3502bb29570b219967278db150a50-Abstract-Conference.html | Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal | https://papers.nips.cc/paper_files/paper/2023/hash/0ff3502bb29570b219967278db150a50-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20617-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0ff3502bb29570b219967278db150a50-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0ff3502bb29570b219967278db150a50-Supplemental-Conference.zip | Offline reinforcement learning (RL) enables learning a decision-making policy without interaction with the environment. This makes it particularly beneficial in situations where such interactions are costly. However, a known challenge for offline RL algorithms is the distributional mismatch between the state-action distributions of the learned policy and the dataset, which can significantly impact performance. State-of-the-art algorithms address it by constraining the policy to align with the state-action pairs in the dataset. However, this strategy struggles on datasets that predominantly consist of trajectories collected by low-performing policies and only a few trajectories from high-performing ones. Indeed, the constraint to align with the data leads the policy to imitate low-performing behaviors predominating the dataset. Our key insight to address this issue is to constrain the policy to the policy that collected the good parts of the dataset rather than all data. To this end, we optimize the importance sampling weights to emulate sampling data from a data distribution generated by a nearly optimal policy. Our method exhibits considerable performance gains (up to five times better) over the existing approaches in state-of-the-art offline RL algorithms over 72 imbalanced datasets with varying types of imbalance. | null |
Variational Weighting for Kernel Density Ratios | https://papers.nips.cc/paper_files/paper/2023/hash/0ff54b4ec4f70b3ae12c8621ca8a49f4-Abstract-Conference.html | Sangwoong Yoon, Frank Park, Gunsu YUN, Iljung Kim, Yung-Kyun Noh | https://papers.nips.cc/paper_files/paper/2023/hash/0ff54b4ec4f70b3ae12c8621ca8a49f4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22867-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0ff54b4ec4f70b3ae12c8621ca8a49f4-Paper-Conference.pdf | null | Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning. Drawing upon tools from the multidimensional calculus of variations, we derive an optimal weight function that reduces bias in standard kernel density estimates for density ratios, leading to improved estimates of prediction posteriors and information-theoretic measures. In the process, we shed light on some fundamental aspects of density estimation, particularly from the perspective of algorithms that employ KDEs as their main building blocks. | null |
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces | https://papers.nips.cc/paper_files/paper/2023/hash/0ffd11b5bce666816802b86c77b54cf7-Abstract-Conference.html | Odelia Melamed, Gilad Yehudai, Gal Vardi | https://papers.nips.cc/paper_files/paper/2023/hash/0ffd11b5bce666816802b86c77b54cf7-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22852-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0ffd11b5bce666816802b86c77b54cf7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0ffd11b5bce666816802b86c77b54cf7-Supplemental-Conference.pdf | Despite a great deal of research, it is still not well-understood why trained neural networks are highly vulnerable to adversarial examples.In this work we focus on two-layer neural networks trained using data which lie on a low dimensional linear subspace.We show that standard gradient methods lead to non-robust neural networks, namely, networks which have large gradients in directions orthogonal to the data subspace, and are susceptible to small adversarial $L_2$-perturbations in these directions.Moreover, we show that decreasing the initialization scale of the training algorithm, or adding $L_2$ regularization, can make the trained network more robust to adversarial perturbations orthogonal to the data. | null |
Complexity of Derivative-Free Policy Optimization for Structured $\mathcal{H}_\infty$ Control | https://papers.nips.cc/paper_files/paper/2023/hash/1052b823a161aa2c808dd51c0f58dc37-Abstract-Conference.html | Xingang Guo, Darioush Keivan, Geir Dullerud, Peter Seiler, Bin Hu | https://papers.nips.cc/paper_files/paper/2023/hash/1052b823a161aa2c808dd51c0f58dc37-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22271-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1052b823a161aa2c808dd51c0f58dc37-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1052b823a161aa2c808dd51c0f58dc37-Supplemental-Conference.zip | The applications of direct policy search in reinforcement learning and continuous control have received increasing attention.In this work, we present novel theoretical results on the complexity of derivative-free policy optimization on an important class of robust control tasks, namely the structured $H_\infty$ synthesis with static output feedback. Optimal $H_\infty$ synthesis under structural constraints leads to a constrained nonconvex nonsmooth problem and is typicallyaddressed using subgradient-based policy search techniques that are built upon the concept of Goldstein subdifferential or other notions of enlarged subdifferential. In this paper, we study the complexity of finding $(\delta,\epsilon)$-stationary points for such nonsmooth robust control design tasks using policy optimization methods which can only access the zeroth-order oracle (i.e. the $H_\infty$ norm of the closed-loop system). First, we study the exact oracle setting and identify the coerciveness of the cost function to prove high-probability feasibility/complexity bounds for derivative-free policy optimization on this problem. Next, we derive a sample complexity result for the multi-input multi-output (MIMO) $H_\infty$-norm estimation. We combine this with our analysis to obtain the first sample complexity of model-free, trajectory-based, zeroth-order policy optimization on finding $(\delta,\epsilon)$-stationary points for structured $H_\infty$ control. Numerical results are also provided to demonstrate our theory. | null |
Meet in the Middle: A New Pre-training Paradigm | https://papers.nips.cc/paper_files/paper/2023/hash/105fdc31cc9eb927cc5a0110f4031287-Abstract-Conference.html | Anh Nguyen, Nikos Karampatziakis, Weizhu Chen | https://papers.nips.cc/paper_files/paper/2023/hash/105fdc31cc9eb927cc5a0110f4031287-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19641-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/105fdc31cc9eb927cc5a0110f4031287-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/105fdc31cc9eb927cc5a0110f4031287-Supplemental-Conference.pdf | Most language models (LMs) are trained and applied in an autoregressive left-to-right fashion, predicting the next token from the preceding ones. However, this ignores that the full sequence is available during training. In this paper, we introduce ``Meet in the Middle'' (MIM) a new pre-training paradigm that improves data efficiency by training in two directions, left-to-right and right-to-left, and encouraging the respective modelsto agree on their token distribution for each position. While the primary outcome is an improved left-to-right LM,we also obtain secondary benefits in the infilling task. There, we leverage the two pre-trained directions to propose an infilling procedure that builds the completion simultaneously from both sides. We conduct extensive experiments on both programming and natural languages and show that MIM significantly surpasses existing pre-training paradigms, in both left-to-right generation as well as infilling.Code and models available at https://github.com/microsoft/Meet-in-the-Middle | null |
Score-based Source Separation with Applications to Digital Communication Signals | https://papers.nips.cc/paper_files/paper/2023/hash/106b2434b8d496c6aed9235d478678af-Abstract-Conference.html | Tejas Jayashankar, Gary C.F. Lee, Alejandro Lancho, Amir Weiss, Yury Polyanskiy, Gregory Wornell | https://papers.nips.cc/paper_files/paper/2023/hash/106b2434b8d496c6aed9235d478678af-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22879-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/106b2434b8d496c6aed9235d478678af-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/106b2434b8d496c6aed9235d478678af-Supplemental-Conference.pdf | We propose a new method for separating superimposed sources using diffusion-based generative models. Our method relies only on separately trained statistical priors of independent sources to establish a new objective function guided by $\textit{maximum a posteriori}$ estimation with an $\textit{$\alpha$-posterior}$, across multiple levels of Gaussian smoothing. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature and the recovery of encoded bits from a signal of interest, as measured by the bit error rate (BER). Experimental results with RF mixtures demonstrate that our method results in a BER reduction of 95\% over classical and existing learning-based methods. Our analysis demonstrates that our proposed method yields solutions that asymptotically approach the modes of an underlying discrete distribution. Furthermore, our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme, shedding additional light on its use beyond conditional sampling. The project webpage is available at https://alpha-rgs.github.io. | null |
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint | https://papers.nips.cc/paper_files/paper/2023/hash/1074541383db5ef12d6ac66d2f8e8d34-Abstract-Conference.html | Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun | https://papers.nips.cc/paper_files/paper/2023/hash/1074541383db5ef12d6ac66d2f8e8d34-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21752-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1074541383db5ef12d6ac66d2f8e8d34-Paper-Conference.pdf | null | Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditional on the sensitive attributes, match one another. However, existing approaches to fair PCA have two main problems: theoretically, there has been no statistical foundation of fair PCA in terms of learnability; practically, limited memory prevents us from using existing approaches, as they explicitly rely on full access to the entire data. On the theoretical side, we rigorously formulate fair PCA using a new notion called probably approximately fair and optimal (PAFO) learnability. On the practical side, motivated by recent advances in streaming algorithms for addressing memory limitation, we propose a new setting called fair streaming PCA along with a memory-efficient algorithm, fair noisy power method (FNPM). We then provide its statistical guarantee in terms of PAFO-learnability, which is the first of its kind in fair PCA literature. We verify our algorithm in the CelebA dataset without any pre-processing; while the existing approaches are inapplicable due to memory limitations, by turning it into a streaming setting, we show that our algorithm performs fair PCA efficiently and effectively. | null |
DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models | https://papers.nips.cc/paper_files/paper/2023/hash/108030643e640ac050e0ed5e6aace48f-Abstract-Conference.html | Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou, Sibei Yang | https://papers.nips.cc/paper_files/paper/2023/hash/108030643e640ac050e0ed5e6aace48f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22480-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/108030643e640ac050e0ed5e6aace48f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/108030643e640ac050e0ed5e6aace48f-Supplemental-Conference.pdf | A long-standing goal of AI systems is to perform complex multimodal reasoning like humans. Recently, large language models (LLMs) have made remarkable strides in such multi-step reasoning on the language modality solely by leveraging the chain of thought (CoT) to mimic human thinking. However, the transfer of these advancements to multimodal contexts introduces heightened challenges, including but not limited to the impractical need for labor-intensive annotation and the limitations in terms of flexibility, generalizability, and explainability. To evoke CoT reasoning in multimodality, this work first conducts an in-depth analysis of these challenges posed by multimodality and presents two key insights: “keeping critical thinking” and “letting everyone do their jobs” in multimodal CoT reasoning. Furthermore, this study proposes a novel DDCoT prompting that maintains a critical attitude through negative-space prompting and incorporates multimodality into reasoning by first dividing the reasoning responsibility of LLMs into reasoning and recognition and then integrating the visual recognition capability of visual models into the joint reasoning process. The rationales generated by DDCoT not only improve the reasoning abilities of both large and small language models in zero-shot prompting and fine-tuning learning, significantly outperforming state-of-the-art methods but also exhibit impressive generalizability and explainability. | null |
Adversarially Robust Learning with Uncertain Perturbation Sets | https://papers.nips.cc/paper_files/paper/2023/hash/1097a0aeaf00cacfa8f6aced24f3a8bd-Abstract-Conference.html | Tosca Lechner, Vinayak Pathak, Ruth Urner | https://papers.nips.cc/paper_files/paper/2023/hash/1097a0aeaf00cacfa8f6aced24f3a8bd-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21570-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1097a0aeaf00cacfa8f6aced24f3a8bd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1097a0aeaf00cacfa8f6aced24f3a8bd-Supplemental-Conference.pdf | In many real-world settings exact perturbation sets to be used by an adversary are not plausibly available to a learner. While prior literature has studied both scenarios with completely known and completely unknown perturbation sets, we propose an in-between setting of learning with respect to a class of perturbation sets. We show that in this setting we can improve on previous results with completely unknown perturbation sets, while still addressing the concerns of not having perfect knowledge of these sets in real life. In particular, we give the first positive results for the learnability of infinite Littlestone classes when having access to a perfect-attack oracle. We also consider a setting of learning with abstention, where predictions are considered robustness violations, only when the wrong prediction is made within the perturbation set. We show there are classes for which perturbation-set unaware learning without query access is possible, but abstention is required. | null |
Common Ground in Cooperative Communication | https://papers.nips.cc/paper_files/paper/2023/hash/10b7e27c8eb9571fbbd2ae6a9f8c3855-Abstract-Conference.html | Xiaoran Hao, Yash Jhaveri, Patrick Shafto | https://papers.nips.cc/paper_files/paper/2023/hash/10b7e27c8eb9571fbbd2ae6a9f8c3855-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21847-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/10b7e27c8eb9571fbbd2ae6a9f8c3855-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/10b7e27c8eb9571fbbd2ae6a9f8c3855-Supplemental-Conference.pdf | Cooperative communication plays a fundamental role in theories of human-human interaction--cognition, culture, development, language, etc.--as well as human-robot interaction. The core challenge in cooperative communication is the problem of common ground: having enough shared knowledge and understanding to successfully communicate. Prior models of cooperative communication, however, uniformly assume the strongest form of common ground, perfect and complete knowledge sharing, and, therefore, fail to capture the core challenge of cooperative communication. We propose a general theory of cooperative communication that is mathematically principled and explicitly defines a spectrum of common ground possibilities, going well beyond that of perfect and complete knowledge sharing, on spaces that permit arbitrary representations of data and hypotheses. Our framework is a strict generalization of prior models of cooperative communication. After considering a parametric form of common ground and viewing the data selection and hypothesis inference processes of communication as encoding and decoding, we establish a connection to variational autoencoding, a powerful model in modern machine learning. Finally, we carry out a series of empirical simulations to support and elaborate on our theoretical results. | null |
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control | https://papers.nips.cc/paper_files/paper/2023/hash/10cb15f4559b3d578b7f24966d48a137-Abstract-Conference.html | Chao Li, Chen GONG, Qiang He, Xinwen Hou | https://papers.nips.cc/paper_files/paper/2023/hash/10cb15f4559b3d578b7f24966d48a137-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19863-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/10cb15f4559b3d578b7f24966d48a137-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/10cb15f4559b3d578b7f24966d48a137-Supplemental-Conference.pdf | The combination of deep reinforcement learning (DRL) with ensemble methods has been proved to be highly effective in addressing complex sequential decision-making problems. This success can be primarily attributed to the utilization of multiple models, which enhances both the robustness of the policy and the accuracy of value function estimation. However, there has been limited analysis of the empirical success of current ensemble RL methods thus far. Our new analysis reveals that the sample efficiency of previous ensemble DRL algorithms may be limited by sub-policies that are not as diverse as they could be. Motivated by these findings, our study introduces a new ensemble RL algorithm, termed \textbf{T}rajectories-awar\textbf{E} \textbf{E}nsemble exploratio\textbf{N} (TEEN). The primary goal of TEEN is to maximize the expected return while promoting more diverse trajectories. Through extensive experiments, we demonstrate that TEEN not only enhances the sample diversity of the ensemble policy compared to using sub-policies alone but also improves the performance over ensemble RL algorithms. On average, TEEN outperforms the baseline ensemble DRL algorithms by 41\% in performance on the tested representative environments. | null |
ReSync: Riemannian Subgradient-based Robust Rotation Synchronization | https://papers.nips.cc/paper_files/paper/2023/hash/10e9204f14c4daa08041343455435308-Abstract-Conference.html | Huikang Liu, Xiao Li, Anthony Man-Cho So | https://papers.nips.cc/paper_files/paper/2023/hash/10e9204f14c4daa08041343455435308-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21611-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/10e9204f14c4daa08041343455435308-Paper-Conference.pdf | null | This work presents ReSync, a Riemannian subgradient-based algorithm for solving the robust rotation synchronization problem, which arises in various engineering applications. ReSync solves a least-unsquared minimization formulation over the rotation group, which is nonsmooth and nonconvex, and aims at recovering the underlying rotations directly. We provide strong theoretical guarantees for ReSync under the random corruption setting. Specifically, we first show that the initialization procedure of ReSync yields a proper initial point that lies in a local region around the ground-truth rotations. We next establish the weak sharpness property of the aforementioned formulation and then utilize this property to derive the local linear convergence of ReSync to the ground-truth rotations. By combining these guarantees, we conclude that ReSync converges linearly to the ground-truth rotations under appropriate conditions. Experiment results demonstrate the effectiveness of ReSync. | null |
On the Exploration of Local Significant Differences For Two-Sample Test | https://papers.nips.cc/paper_files/paper/2023/hash/10fc83943b4540a9524af6fc67a23fef-Abstract-Conference.html | Zhijian Zhou, Jie Ni, Jia-He Yao, Wei Gao | https://papers.nips.cc/paper_files/paper/2023/hash/10fc83943b4540a9524af6fc67a23fef-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19802-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/10fc83943b4540a9524af6fc67a23fef-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/10fc83943b4540a9524af6fc67a23fef-Supplemental-Conference.zip | Recent years have witnessed increasing attentions on two-sample test with diverse real applications, while this work takes one more step on the exploration of local significant differences for two-sample test. We propose the ME$_\text{MaBiD}$, an effective test for two-sample testing, and the basic idea is to exploit local information by multiple Mahalanobis kernels and introduce bi-directional hypothesis for testing. On the exploration of local significant differences, we first partition the embedding space into several rectangle regions via a new splitting criterion, which is relevant to test power and data correlation. We then explore local significant differences based on our bi-directional masked $p$-value together with the ME$_\text{MaBiD}$ test. Theoretically, we present the asymptotic distribution and lower bounds of test power for our ME$_\text{MaBiD}$ test, and control the familywise error rate on the exploration of local significant differences. We finally conduct extensive experiments to validate the effectiveness of our proposed methods on two-sample test and the exploration of local significant differences. | null |
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator | https://papers.nips.cc/paper_files/paper/2023/hash/112d8e0c7563de6e3408b49a09b4d8a3-Abstract-Conference.html | Xiaolong Wang, Runsen Xu, Zhuofan Cui, Zeyu Wan, Yu Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/112d8e0c7563de6e3408b49a09b4d8a3-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21857-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/112d8e0c7563de6e3408b49a09b4d8a3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/112d8e0c7563de6e3408b49a09b4d8a3-Supplemental-Conference.pdf | In this paper, we introduce a novel approach to fine-grained cross-view geo-localization. Our method aligns a warped ground image with a corresponding GPS-tagged satellite image covering the same area using homography estimation. We first employ a differentiable spherical transform, adhering to geometric principles, to accurately align the perspective of the ground image with the satellite map. This transformation effectively places ground and aerial images in the same view and on the same plane, reducing the task to an image alignment problem. To address challenges such as occlusion, small overlapping range, and seasonal variations, we propose a robust correlation-aware homography estimator to align similar parts of the transformed ground image with the satellite image. Our method achieves sub-pixel resolution and meter-level GPS accuracy by mapping the center point of the transformed ground image to the satellite image using a homography matrix and determining the orientation of the ground camera using a point above the central axis. Operating at a speed of 30 FPS, our method outperforms state-of-the-art techniques, reducing the mean metric localization error by 21.3\% and 32.4\% in same-area and cross-area generalization tasks on the VIGOR benchmark, respectively, and by 34.4\% on the KITTI benchmark in same-area evaluation. | null |
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization | https://papers.nips.cc/paper_files/paper/2023/hash/1160792eab11de2bbaf9e71fce191e8c-Abstract-Conference.html | Quanqi Hu, Dixian Zhu, Tianbao Yang | https://papers.nips.cc/paper_files/paper/2023/hash/1160792eab11de2bbaf9e71fce191e8c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20680-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1160792eab11de2bbaf9e71fce191e8c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1160792eab11de2bbaf9e71fce191e8c-Supplemental-Conference.zip | This paper investigates new families of compositional optimization problems, called non-smooth weakly-convex finite-sum coupled compositional optimization (NSWC FCCO). There has been a growing interest in FCCO due to its wide-ranging applications in machine learning and AI, as well as its ability to address the shortcomings of stochastic algorithms based on empirical risk minimization. However, current research on FCCO presumes that both the inner and outer functions are smooth, limiting their potential to tackle a more diverse set of problems. Our research expands on this area by examining non-smooth weakly-convex FCCO, where the outer function is weakly convex and non-decreasing, and the inner function is weakly-convex. We analyze a single-loop algorithm and establish its complexity for finding an $\epsilon$-stationary point of the Moreau envelop of the objective function. Additionally, we also extend the algorithm for solving novel non-smooth weakly-convex tri-level finite-sum coupled compositional optimization problems, which feature a nested arrangement of three functions. Lastly, we explore the applications of our algorithms in deep learning for two-way partial AUC maximization and multi-instance two-way partial AUC maximization, using empirical studies to showcase the effectiveness of the proposed algorithms. | null |
Optimal Transport for Treatment Effect Estimation | https://papers.nips.cc/paper_files/paper/2023/hash/1160e7f31d0a74abbbe1bbf7924b949c-Abstract-Conference.html | Hao Wang, Jiajun Fan, Zhichao Chen, Haoxuan Li, Weiming Liu, Tianqiao Liu, Quanyu Dai, Yichao Wang, Zhenhua Dong, Ruiming Tang | https://papers.nips.cc/paper_files/paper/2023/hash/1160e7f31d0a74abbbe1bbf7924b949c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21490-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1160e7f31d0a74abbbe1bbf7924b949c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1160e7f31d0a74abbbe1bbf7924b949c-Supplemental-Conference.zip | Estimating individual treatment effects from observational data is challenging due to treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different treatment groups in the latent space, the core of which is the calculation of distribution discrepancy. However, two issues that are often overlooked can render these methods invalid:(1) mini-batch sampling effects (MSE), where the calculated discrepancy is erroneous in non-ideal mini-batches with outcome imbalance and outliers;(2) unobserved confounder effects (UCE), where the unobserved confounders are not considered in the discrepancy calculation.Both of these issues invalidate the calculated discrepancy, mislead the training of estimators, and thus impede the handling of treatment selection bias.To tackle these issues, we propose Entire Space CounterFactual Regression (ESCFR), which is a new take on optimal transport technology in the context of causality.Specifically, based on the canonical optimal transport framework, we propose a relaxed mass-preserving regularizer to address the MSE issue and design a proximal factual outcome regularizer to handle the UCE issue.Extensive experiments demonstrate that ESCFR estimates distribution discrepancy accurately, handles the treatment selection bias effectively, and outperforms prevalent competitors significantly. | null |
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks | https://papers.nips.cc/paper_files/paper/2023/hash/1165af8b913fb836c6280b42d6e0084f-Abstract-Conference.html | Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher | https://papers.nips.cc/paper_files/paper/2023/hash/1165af8b913fb836c6280b42d6e0084f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22043-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1165af8b913fb836c6280b42d6e0084f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1165af8b913fb836c6280b42d6e0084f-Supplemental-Conference.pdf | We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, we prove a privacy bound for the KL divergence between model distributions on worst-case neighboring datasets, and explore its dependence on the initialization, width, and depth of fully connected neural networks. We find that this KL privacy bound is largely determined by the expected squared gradient norm relative to model parameters during training. Notably, for the special setting of linearized network, our analysis indicates that the squared gradient norm (and therefore the escalation of privacy loss) is tied directly to the per-layer variance of the initialization distribution. By using this analysis, we demonstrate that privacy bound improves with increasing depth under certain initializations (LeCun and Xavier), while degrades with increasing depth under other initializations (He and NTK). Our work reveals a complex interplay between privacy and depth that depends on the chosen initialization distribution. We further prove excess empirical risk bounds under a fixed KL privacy budget, and show that the interplay between privacy utility trade-off and depth is similarly affected by the initialization. | null |
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing | https://papers.nips.cc/paper_files/paper/2023/hash/11715d433f6f8b9106baae0df023deb3-Abstract-Conference.html | Xiangyu Sun, Oliver Schulte | https://papers.nips.cc/paper_files/paper/2023/hash/11715d433f6f8b9106baae0df023deb3-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21089-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/11715d433f6f8b9106baae0df023deb3-Paper-Conference.pdf | null | A fundamental problem of causal discovery is cause-effect inference, to learn the correct causal direction between two random variables. Significant progress has been made through modelling the effect as a function of its cause and a noise term, which allows us to leverage assumptions about the generating function class. The recently introduced heteroscedastic location-scale noise functional models (LSNMs) combine expressive power with identifiability guarantees. LSNM model selection based on maximizing likelihood achieves state-of-the-art accuracy, when the noise distributions are correctly specified. However, through an extensive empirical evaluation, we demonstrate that the accuracy deteriorates sharply when the form of the noise distribution is misspecified by the user. Our analysis shows that the failure occurs mainly when the conditional variance in the anti-causal direction is smaller than that in the causal direction. As an alternative, we find that causal model selection through residual independence testing is much more robust to noise misspecification and misleading conditional variance. | null |
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders | https://papers.nips.cc/paper_files/paper/2023/hash/11822e84689e631615199db3b75cd0e4-Abstract-Conference.html | Anthony Fuller, Koreen Millard, James Green | https://papers.nips.cc/paper_files/paper/2023/hash/11822e84689e631615199db3b75cd0e4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20458-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/11822e84689e631615199db3b75cd0e4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/11822e84689e631615199db3b75cd0e4-Supplemental-Conference.zip | A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multimodal representations. Our method separately encodes masked-out multispectral optical and synthetic aperture radar samples—aligned in space and time—and performs cross-modal contrastive learning. Another encoder fuses these sensors, producing joint multimodal encodings that are used to predict the masked patches via a lightweight decoder. We show that these objectives are complementary when leveraged on spatially aligned multimodal data. We also introduce X- and 2D-ALiBi, which spatially biases our cross- and self-attention matrices. These strategies improve representations and allow our models to effectively extrapolate to images up to $17.6\times$ larger at test-time. CROMA outperforms the current SoTA multispectral model, evaluated on: four classification benchmarks—finetuning (avg.$\uparrow$ 1.8%), linear (avg.$\uparrow$ 2.4%) and nonlinear (avg.$\uparrow$ 1.4%) probing, $k$NN classification (avg.$\uparrow$ 3.5%), and $K$-means clustering (avg.$\uparrow$ 8.4%); and three segmentation benchmarks (avg.$\uparrow$ 6.4%). CROMA’s rich, optionally multimodal representations can be widely leveraged across remote sensing applications. | null |
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions | https://papers.nips.cc/paper_files/paper/2023/hash/11a7f429d75f9f8c6e9c630aeb6524b5-Abstract-Conference.html | Ruofan Wu, Jiawei Qiao, Mingzhe Wu, Wen Yu, Ming Zheng, Tengfei LIU, Tianyi Zhang, Weiqiang Wang | https://papers.nips.cc/paper_files/paper/2023/hash/11a7f429d75f9f8c6e9c630aeb6524b5-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21494-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/11a7f429d75f9f8c6e9c630aeb6524b5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/11a7f429d75f9f8c6e9c630aeb6524b5-Supplemental-Conference.zip | We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence. Two concrete models are derived under the framework that extends neural proportional hazard models and nonparametric hazard regression models. Both models allow efficient training under the likelihood objective. Theoretically, for both proposed models, we establish statistical guarantees of neural function approximation with respect to nonparametric components via characterizing their rate of convergence. Empirically, we provide synthetic experiments that verify our theoretical statements. We also conduct experimental evaluations over $6$ benchmark datasets of different scales, showing that the proposed NFM models achieve predictive performance comparable to or sometimes surpassing state-of-the-art survival models. Our code is publicly availabel at https://github.com/Rorschach1989/nfm | null |
Non-autoregressive Machine Translation with Probabilistic Context-free Grammar | https://papers.nips.cc/paper_files/paper/2023/hash/11c7f1dd168439884b6dfb43a7891432-Abstract-Conference.html | Shangtong Gui, Chenze Shao, Zhengrui Ma, xishan zhang, Yunji Chen, Yang Feng | https://papers.nips.cc/paper_files/paper/2023/hash/11c7f1dd168439884b6dfb43a7891432-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20876-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/11c7f1dd168439884b6dfb43a7891432-Paper-Conference.pdf | null | Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation. However, conventional NAT models suffer from limited expression power and performance degradation compared to autoregressive (AT) models due to the assumption of conditional independence among target tokens. To address these limitations, we propose a novel approach called PCFG-NAT, which leverages a specially designed Probabilistic Context-Free Grammar (PCFG) to enhance the ability of NAT models to capture complex dependencies among output tokens. Experimental results on major machine translation benchmarks demonstrate that PCFG-NAT further narrows the gap in translation quality between NAT and AT models. Moreover, PCFG-NAT facilitates a deeper understanding of the generated sentences, addressing the lack of satisfactory explainability in neural machine translation. Code is publicly available at https://github.com/ictnlp/PCFG-NAT. | null |
Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2023/hash/11e1900e680f5fe1893a8e27362dbe2c-Abstract-Conference.html | Jing Zhang, Chi Zhang, Wenjia Wang, Bingyi Jing | https://papers.nips.cc/paper_files/paper/2023/hash/11e1900e680f5fe1893a8e27362dbe2c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21268-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/11e1900e680f5fe1893a8e27362dbe2c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/11e1900e680f5fe1893a8e27362dbe2c-Supplemental-Conference.pdf | Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points. Existing methods for addressing this issue either control policy to exclude the OOD action or make the $Q$ function pessimistic. However, these methods can be overly conservative or fail to identify OOD areas accurately. To overcome this problem, we propose a Constrained Policy optimization with Explicit Behavior density (CPED) method that utilizes a flow-GAN model to explicitly estimate the density of behavior policy. By estimating the explicit density, CPED can accurately identify the safe region and enable exploration within the region, resulting in less conservative learning policies. We further provide theoretical results for both the flow-GAN estimator and performance guarantee for CPED by showing that CPED can find the optimal $Q$-function value. Empirically, CPED outperforms existing alternatives on various standard offline reinforcement learning tasks, yielding higher expected returns. | null |
Formalizing locality for normative synaptic plasticity models | https://papers.nips.cc/paper_files/paper/2023/hash/120339238f293d4ae53a7167403abc4b-Abstract-Conference.html | Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake Richards, Guillaume Lajoie | https://papers.nips.cc/paper_files/paper/2023/hash/120339238f293d4ae53a7167403abc4b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21103-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/120339238f293d4ae53a7167403abc4b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/120339238f293d4ae53a7167403abc4b-Supplemental-Conference.pdf | In recent years, many researchers have proposed new models for synaptic plasticity in the brain based on principles of machine learning. The central motivation has been the development of learning algorithms that are able to learn difficult tasks while qualifying as "biologically plausible". However, the concept of a biologically plausible learning algorithm is only heuristically defined as an algorithm that is potentially implementable by biological neural networks. Further, claims that neural circuits could implement any given algorithm typically rest on an amorphous concept of "locality" (both in space and time). As a result, it is unclear what many proposed local learning algorithms actually predict biologically, and which of these are consequently good candidates for experimental investigation. Here, we address this lack of clarity by proposing formal and operational definitions of locality. Specifically, we define different classes of locality, each of which makes clear what quantities cannot be included in a learning rule if an algorithm is to qualify as local with respect to a given (biological) constraint. We subsequently use this framework to distill testable predictions from various classes of biologically plausible synaptic plasticity models that are robust to arbitrary choices about neural network architecture. Therefore, our framework can be used to guide claims of biological plausibility and to identify potential means of experimentally falsifying a proposed learning algorithm for the brain. | null |
Exact Verification of ReLU Neural Control Barrier Functions | https://papers.nips.cc/paper_files/paper/2023/hash/120ed726cf129dbeb8375b6f8a0686f8-Abstract-Conference.html | Hongchao Zhang, Junlin Wu, Yevgeniy Vorobeychik, Andrew Clark | https://papers.nips.cc/paper_files/paper/2023/hash/120ed726cf129dbeb8375b6f8a0686f8-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22009-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/120ed726cf129dbeb8375b6f8a0686f8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/120ed726cf129dbeb8375b6f8a0686f8-Supplemental-Conference.pdf | Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the CBF remains nonnegative for all time. Recently, machine learning methods that represent CBFs as neural networks (neural control barrier functions, or NCBFs) have shown great promise due to the universal representability of neural networks. However, verifying that a learned CBF guarantees safety remains a challenging research problem. This paper presents novel exact conditions and algorithms for verifying safety of feedforward NCBFs with ReLU activation functions. The key challenge in doing so is that, due to the piecewise linearity of the ReLU function, the NCBF will be nondifferentiable at certain points, thus invalidating traditional safety verification methods that assume a smooth barrier function. We resolve this issue by leveraging a generalization of Nagumo's theorem for proving invariance of sets with nonsmooth boundaries to derive necessary and sufficient conditions for safety. Based on this condition, we propose an algorithm for safety verification of NCBFs that first decomposes the NCBF into piecewise linear segments and then solves a nonlinear program to verify safety of each segment as well as the intersections of the linear segments. We mitigate the complexity by only considering the boundary of the safe region and by pruning the segments with Interval Bound Propagation (IBP) and linear relaxation. We evaluate our approach through numerical studies with comparison to state-of-the-art SMT-based methods. Our code is available at https://github.com/HongchaoZhang-HZ/exactverif-reluncbf-nips23. | null |
Normalization-Equivariant Neural Networks with Application to Image Denoising | https://papers.nips.cc/paper_files/paper/2023/hash/12143893d9d37c3569dda800b95cabd9-Abstract-Conference.html | Sébastien Herbreteau, Emmanuel Moebel, Charles Kervrann | https://papers.nips.cc/paper_files/paper/2023/hash/12143893d9d37c3569dda800b95cabd9-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21948-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/12143893d9d37c3569dda800b95cabd9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/12143893d9d37c3569dda800b95cabd9-Supplemental-Conference.pdf | In many information processing systems, it may be desirable to ensure that any change of the input, whether by shifting or scaling, results in a corresponding change in the system response. While deep neural networks are gradually replacing all traditional automatic processing methods, they surprisingly do not guarantee such normalization-equivariance (scale + shift) property, which can be detrimental in many applications. To address this issue, we propose a methodology for adapting existing neural networks so that normalization-equivariance holds by design. Our main claim is that not only ordinary convolutional layers, but also all activation functions, including the ReLU (rectified linear unit), which are applied element-wise to the pre-activated neurons, should be completely removed from neural networks and replaced by better conditioned alternatives. To this end, we introduce affine-constrained convolutions and channel-wise sort pooling layers as surrogates and show that these two architectural modifications do preserve normalization-equivariance without loss of performance. Experimental results in image denoising show that normalization-equivariant neural networks, in addition to their better conditioning, also provide much better generalization across noise levels. | null |
Budgeting Counterfactual for Offline RL | https://papers.nips.cc/paper_files/paper/2023/hash/121db870b0470dd63bb5bc59c724275a-Abstract-Conference.html | Yao Liu, Pratik Chaudhari, Rasool Fakoor | https://papers.nips.cc/paper_files/paper/2023/hash/121db870b0470dd63bb5bc59c724275a-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21755-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/121db870b0470dd63bb5bc59c724275a-Paper-Conference.pdf | null | The main challenge of offline reinforcement learning, where data is limited, arises from a sequence of counterfactual reasoning dilemmas within the realm of potential actions: What if we were to choose a different course of action? These circumstances frequently give rise to extrapolation errors, which tend to accumulate exponentially with the problem horizon. Hence, it becomes crucial to acknowledge that not all decision steps are equally important to the final outcome, and to budget the number of counterfactual decisions a policy make in order to control the extrapolation. Contrary to existing approaches that use regularization on either the policy or value function, we propose an approach to explicitly bound the amount of out-of-distribution actions during training. Specifically, our method utilizes dynamic programming to decide where to extrapolate and where not to, with an upper bound on the decisions different from behavior policy. It balances between the potential for improvement from taking out-of-distribution actions and the risk of making errors due to extrapolation. Theoretically, we justify our method by the constrained optimality of the fixed point solution to our $Q$ updating rules. Empirically, we show that the overall performance of our method is better than the state-of-the-art offline RL methods on tasks in the widely-used D4RL benchmarks. | null |
Federated Conditional Stochastic Optimization | https://papers.nips.cc/paper_files/paper/2023/hash/1229eaae5bf1db93e1e4c539258eb472-Abstract-Conference.html | Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang | https://papers.nips.cc/paper_files/paper/2023/hash/1229eaae5bf1db93e1e4c539258eb472-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22715-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1229eaae5bf1db93e1e4c539258eb472-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1229eaae5bf1db93e1e4c539258eb472-Supplemental-Conference.pdf | Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the demand for training models with large-scale distributed data grows in these applications, there is an increasing need for communication-efficient distributed optimization algorithms, such as federated learning algorithms. This paper considers the nonconvex conditional stochastic optimization in federated learning and proposes the first federated conditional stochastic optimization algorithm (FCSG) with a conditional stochastic gradient estimator and a momentum-based algorithm (\emph{i.e.}, FCSG-M). To match the lower bound complexity in the single-machine setting, we design an accelerated algorithm (Acc-FCSG-M) via the variance reduction to achieve the best sample and communication complexity. Compared with the existing optimization analysis for Meta-Learning in FL, federated conditional stochastic optimization considers the sample of tasks. Extensive experimental results on various tasks validate the efficiency of these algorithms. | null |
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections | https://papers.nips.cc/paper_files/paper/2023/hash/123a18dfd821c8b440f42a00a27648d6-Abstract-Conference.html | Muhammad Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Horst Possegger, Mateusz Kozinski, Rogerio Feris, Horst Bischof | https://papers.nips.cc/paper_files/paper/2023/hash/123a18dfd821c8b440f42a00a27648d6-Abstract-Conference.html | NIPS 2023 | null | null | null | null | null |
Contextually Affinitive Neighborhood Refinery for Deep Clustering | https://papers.nips.cc/paper_files/paper/2023/hash/123cfe7d8b7702ac97aaf4468fc05fa5-Abstract-Conference.html | Chunlin Yu, Ye Shi, Jingya Wang | https://papers.nips.cc/paper_files/paper/2023/hash/123cfe7d8b7702ac97aaf4468fc05fa5-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22937-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/123cfe7d8b7702ac97aaf4468fc05fa5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/123cfe7d8b7702ac97aaf4468fc05fa5-Supplemental-Conference.pdf | Previous endeavors in self-supervised learning have enlightened the research of deep clustering from an instance discrimination perspective. Built upon this foundation, recent studies further highlight the importance of grouping semantically similar instances. One effective method to achieve this is by promoting the semantic structure preserved by neighborhood consistency. However, the samples in the local neighborhood may be limited due to their close proximity to each other, which may not provide substantial and diverse supervision signals. Inspired by the versatile re-ranking methods in the context of image retrieval, we propose to employ an efficient online re-ranking process to mine more informative neighbors in a Contextually Affinitive (ConAff) Neighborhood, and then encourage the cross-view neighborhood consistency. To further mitigate the intrinsic neighborhood noises near cluster boundaries, we propose a progressively relaxed boundary filtering strategy to circumvent the issues brought by noisy neighbors. Our method can be easily integrated into the generic self-supervised frameworks and outperforms the state-of-the-art methods on several popular benchmarks. | null |
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives | https://papers.nips.cc/paper_files/paper/2023/hash/123fd8a56501194823c8e0dca00733df-Abstract-Conference.html | Tom Monnier, Jake Austin, Angjoo Kanazawa, Alexei Efros, Mathieu Aubry | https://papers.nips.cc/paper_files/paper/2023/hash/123fd8a56501194823c8e0dca00733df-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20052-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/123fd8a56501194823c8e0dca00733df-Paper-Conference.pdf | null | Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations. Code and video results are available at https://www.tmonnier.com/DBW. | null |
Learning Shared Safety Constraints from Multi-task Demonstrations | https://papers.nips.cc/paper_files/paper/2023/hash/124dde499d62b58e97e42a45b26d7369-Abstract-Conference.html | Konwoo Kim, Gokul Swamy, ZUXIN LIU, DING ZHAO, Sanjiban Choudhury, Steven Z. Wu | https://papers.nips.cc/paper_files/paper/2023/hash/124dde499d62b58e97e42a45b26d7369-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21155-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/124dde499d62b58e97e42a45b26d7369-Paper-Conference.pdf | null | Regardless of the particular task we want to perform in an environment, there are often shared safety constraints we want our agents to respect. For example, regardless of whether it is making a sandwich or clearing the table, a kitchen robot should not break a plate. Manually specifying such a constraint can be both time-consuming and error-prone. We show how to learn constraints from expert demonstrations of safe task completion by extending inverse reinforcement learning (IRL) techniques to the space of constraints. Intuitively, we learn constraints that forbid highly rewarding behavior that the expert could have taken but chose not to. Unfortunately, the constraint learning problem is rather ill-posed and typically leads to overly conservative constraints that forbid all behavior that the expert did not take. We counter this by leveraging diverse demonstrations that naturally occur in multi-task setting to learn a tighter set of constraints. We validate our method with simulation experiments on high-dimensional continuous control tasks. | null |
Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner | https://papers.nips.cc/paper_files/paper/2023/hash/1289f9195d2ef8cfdfe5f50930c4a7c4-Abstract-Conference.html | Zhengxiang Shi, Aldo Lipani | https://papers.nips.cc/paper_files/paper/2023/hash/1289f9195d2ef8cfdfe5f50930c4a7c4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21456-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1289f9195d2ef8cfdfe5f50930c4a7c4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1289f9195d2ef8cfdfe5f50930c4a7c4-Supplemental-Conference.pdf | Language models (LMs) trained on vast quantities of unlabelled data have greatly advanced the field of natural language processing (NLP). In this study, we re-visit the widely accepted notion in NLP that continued pre-training LMs on task-related texts improves the performance of fine-tuning (FT) in downstream tasks. Through experiments on eight single-sentence tasks and eight sentence-pair tasks in both semi-supervised and fully-supervised settings, we find that conventional continued pre-training does not consistently provide benefits and can even be detrimental for sentence-pair tasks or when prompt-based FT is used. To tackle these issues, we propose Prompt-based Continued Pre-training (PCP), which combines the idea of instruction tuning with conventional continued pre-training. Our approach aims to improve the performance of prompt-based FT by presenting both task-related texts and prompt templates to LMs through unsupervised pre-training objectives before fine-tuning for the target task. Our empirical evaluations on 21 benchmarks demonstrate that the PCP consistently improves the performance of state-of-the-art prompt-based FT approaches (up to 20.1% absolute) in both semi-supervised and fully-supervised settings, even with only hundreds of unlabelled examples. Additionally, prompt-based FT with PCP outperforms state-of-the-art semi-supervised approaches with greater simplicity, eliminating the need for an iterative process and extra data augmentation. Our further analysis explores the performance lower bound of the PCP and reveals that the advantages of PCP persist across different sizes of models and datasets. | null |
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning | https://papers.nips.cc/paper_files/paper/2023/hash/129033c7c08be683059559e8d6bfd460-Abstract-Conference.html | Haiteng Zhao, Shengchao Liu, Ma Chang, Hannan Xu, Jie Fu, Zhihong Deng, Lingpeng Kong, Qi Liu | https://papers.nips.cc/paper_files/paper/2023/hash/129033c7c08be683059559e8d6bfd460-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22664-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/129033c7c08be683059559e8d6bfd460-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/129033c7c08be683059559e8d6bfd460-Supplemental-Conference.zip | Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv. | null |
GEX: A flexible method for approximating influence via Geometric Ensemble | https://papers.nips.cc/paper_files/paper/2023/hash/1297ca5c906f4bada8f5f6f4e80f9dd2-Abstract-Conference.html | SungYub Kim, Kyungsu Kim, Eunho Yang | https://papers.nips.cc/paper_files/paper/2023/hash/1297ca5c906f4bada8f5f6f4e80f9dd2-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20608-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1297ca5c906f4bada8f5f6f4e80f9dd2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1297ca5c906f4bada8f5f6f4e80f9dd2-Supplemental-Conference.pdf | Through a deeper understanding of predictions of neural networks, Influence Function (IF) has been applied to various tasks such as detecting and relabeling mislabeled samples, dataset pruning, and separation of data sources in practice. However, we found standard approximations of IF suffer from performance degradation due to oversimplified influence distributions caused by their bilinear approximation, suppressing the expressive power of samples with a relatively strong influence. To address this issue, we propose a new interpretation of existing IF approximations as an average relationship between two linearized losses over parameters sampled from the Laplace approximation (LA). In doing so, we highlight two significant limitations of current IF approximations: the linearity of gradients and the singularity of Hessian. Accordingly, by improving each point, we introduce a new IF approximation method with the following features: i) the removal of linearization to alleviate the bilinear constraint and ii) the utilization of Geometric Ensemble (GE) tailored for non-linear losses. Empirically, our approach outperforms existing IF approximations for downstream tasks with lighter computation, thereby providing new feasibility of low-complexity/nonlinear-based IF design. | null |
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management | https://papers.nips.cc/paper_files/paper/2023/hash/12bcf58a1c09a0fcb5310f3589291ab4-Abstract-Conference.html | Dhawal Gupta, Yinlam Chow, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier | https://papers.nips.cc/paper_files/paper/2023/hash/12bcf58a1c09a0fcb5310f3589291ab4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21843-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/12bcf58a1c09a0fcb5310f3589291ab4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/12bcf58a1c09a0fcb5310f3589291ab4-Supplemental-Conference.pdf | Reinforcement learning (RL) has shown great promise for developing agents for dialogue management (DM) that are non-myopic, conduct rich conversations, and maximize overall user satisfaction. Despite the advancements in RL and language models (LMs), employing RL to drive conversational chatbots still poses significant challenges. A primary issue stems from RL’s dependency on online exploration for effective learning, a process that can be costly. Moreover, engaging in online interactions with humans during the training phase can raise safety concerns, as the LM can potentially generate unwanted outputs. This issue is exacerbated by the combinatorial action spaces facing these algorithms, as most LM agents generate responses at the word level. We develop various RL algorithms, specialized in dialogue planning, that leverage recent Mixture-of-Expert Language Models (MoE-LMs)---models that capture diverse semantics, generate utterances reflecting different intents, and are amenable for multi-turn DM. By exploiting the MoE-LM structure, our methods significantly reduce the size of the action space and improve the efficacy of RL-based DM. We evaluate our methods in open-domain dialogue to demonstrate their effectiveness with respect to the diversity of intent in generated utterances and overall DM performance. | null |
Binary Classification with Confidence Difference | https://papers.nips.cc/paper_files/paper/2023/hash/12c118ef87fde56a10bd858842781b34-Abstract-Conference.html | Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama | https://papers.nips.cc/paper_files/paper/2023/hash/12c118ef87fde56a10bd858842781b34-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19888-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/12c118ef87fde56a10bd858842781b34-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/12c118ef87fde56a10bd858842781b34-Supplemental-Conference.zip | Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. However, collecting pointwise labeling confidence for all training examples can be challenging and time-consuming in real-world scenarios. This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. Instead of pointwise labeling confidence, we are given only unlabeled data pairs with confidence difference that specifies the difference in the probabilities of being positive. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound achieves the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven. Extensive experiments on benchmark data sets and a real-world recommender system data set validate the effectiveness of our proposed approaches in exploiting the supervision information of the confidence difference. | null |
On student-teacher deviations in distillation: does it pay to disobey? | https://papers.nips.cc/paper_files/paper/2023/hash/12d286282e1be5431ea05262a21f415c-Abstract-Conference.html | Vaishnavh Nagarajan, Aditya K. Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar | https://papers.nips.cc/paper_files/paper/2023/hash/12d286282e1be5431ea05262a21f415c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21316-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/12d286282e1be5431ea05262a21f415c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/12d286282e1be5431ea05262a21f415c-Supplemental-Conference.pdf | Knowledge distillation (KD) has been widely used to improve the test accuracy of a "student" network, by training it to mimic the soft probabilities of a trained "teacher" network. Yet, it has been shown in recent work that, despite being trained to fit the teacher's probabilities, the student may not only significantly deviate from the teacher probabilities, but may also outdo than the teacher in performance. Our work aims to reconcile this seemingly paradoxical observation. Specifically, we characterize the precise nature of the student-teacher deviations, and argue how they can co-occur with better generalization. First, through experiments on image and language data, we identify that these probability deviations correspond to the student systematically exaggerating the confidence levels of the teacher.Next, we theoretically and empirically establish another form of exaggeration in some simple settings: KD exaggerates the implicit bias of gradient descent in converging faster along the top eigendirections of the data. Finally, we tie these two observations together: we demonstrate that the exaggerated bias of KD can simultaneously result in both (a) the exaggeration of confidence and (b) the improved generalization of the student, thus offering a resolution to the apparent paradox. Our analysis brings existing theory and practice closer by considering the role of gradient descent in KD and by demonstrating the exaggerated bias effect in both theoretical and empirical settings. | null |
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis | https://papers.nips.cc/paper_files/paper/2023/hash/12d7ba753894ed348904df1bf0ce02ec-Abstract-Conference.html | Victor Letzelter, Mathieu Fontaine, Mickael Chen, Patrick Pérez, Slim Essid, Gaël Richard | https://papers.nips.cc/paper_files/paper/2023/hash/12d7ba753894ed348904df1bf0ce02ec-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21296-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/12d7ba753894ed348904df1bf0ce02ec-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/12d7ba753894ed348904df1bf0ce02ec-Supplemental-Conference.zip | We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL approach for conditional distribution estimation in regression settings where multiple targets may be sampled for each training input.Multiple Choice Learning is a simple framework to tackle multimodal density estimation, using the Winner-Takes-All (WTA) loss for a set of hypotheses. In regression settings, the existing MCL variants focus on merging the hypotheses, thereby eventually sacrificing the diversity of the predictions. In contrast, our method relies on a novel learned scoring scheme underpinned by a mathematical framework based on Voronoi tessellations of the output space, from which we can derive a probabilistic interpretation.After empirically validating rMCL with experiments on synthetic data, we further assess its merits on the sound source localization problem, demonstrating its practical usefulness and the relevance of its interpretation. | null |
Graph of Circuits with GNN for Exploring the Optimal Design Space | https://papers.nips.cc/paper_files/paper/2023/hash/12da92b7c64176eb6eb6ad0ae31554fd-Abstract-Conference.html | Aditya Shahane, Saripilli Swapna Manjiri, Ankesh Jain, Sandeep Kumar | https://papers.nips.cc/paper_files/paper/2023/hash/12da92b7c64176eb6eb6ad0ae31554fd-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22503-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/12da92b7c64176eb6eb6ad0ae31554fd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/12da92b7c64176eb6eb6ad0ae31554fd-Supplemental-Conference.zip | The design automation of analog circuits poses significant challenges in terms of the large design space, complex interdependencies between circuit specifications, and resource-intensive simulations. To address these challenges, this paper presents an innovative framework called the Graph of Circuits Explorer (GCX). Leveraging graph structure learning along with graph neural networks, GCX enables the creation of a surrogate model that facilitates efficient exploration of the optimal design space within a semi-supervised learning framework which reduces the need for large labelled datasets. The proposed approach comprises three key stages. First, we learn the geometric representation of circuits and enrich it with technology information to create a comprehensive feature vector. Subsequently, integrating feature-based graph learning with few-shot and zero-shot learning enhances the generalizability in predictions for unseen circuits. Finally, we introduce two algorithms namely, EASCO and ASTROG which upon integration with GCX optimize the available samples to yield the optimal circuit configuration meeting the designer's criteria. The effectiveness of the proposed approach is demonstrated through simulated performance evaluation of various circuits, using derived parameters in 180nm CMOS technology. Furthermore, the generalizability of the approach is extended to higher-order topologies and different technology nodes such as 65nm and 45nm CMOS process nodes. | null |
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data | https://papers.nips.cc/paper_files/paper/2023/hash/13183a224208671a6fc33ba1aa661ec4-Abstract-Conference.html | Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan | https://papers.nips.cc/paper_files/paper/2023/hash/13183a224208671a6fc33ba1aa661ec4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20559-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/13183a224208671a6fc33ba1aa661ec4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/13183a224208671a6fc33ba1aa661ec4-Supplemental-Conference.pdf | Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks.However, existing graph condensation methods rely on the joint optimization of nodes and structures in the condensed graph, and overlook critical issues in effectiveness and generalization ability.In this paper, we advocate a new Structure-Free Graph Condensation paradigm, named SFGC, to distill a large-scale graph into a small-scale graph node set without explicit graph structures, i.e., graph-free data.Our idea is to implicitly encode topology structure information into the node attributes in the synthesized graph-free data, whose topology is reduced to an identity matrix.Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data;(2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data. Through training trajectory meta-matching, SFGC aligns the long-term GNN learning behaviors between the large-scale graph and the condensed small-scale graph-free data, ensuring comprehensive and compact transfer of informative knowledge to the graph-free data.Afterward, the underlying condensed graph-free data would be dynamically evaluated with the graph neural feature score, which is a closed-form metric for ensuring the excellent expressiveness of the condensed graph-free data.Extensive experiments verify the superiority of SFGC across different condensation ratios. | null |
Visual Programming for Step-by-Step Text-to-Image Generation and Evaluation | https://papers.nips.cc/paper_files/paper/2023/hash/13250eb13871b3c2c0a0667b54bad165-Abstract-Conference.html | Jaemin Cho, Abhay Zala, Mohit Bansal | https://papers.nips.cc/paper_files/paper/2023/hash/13250eb13871b3c2c0a0667b54bad165-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21764-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/13250eb13871b3c2c0a0667b54bad165-Paper-Conference.pdf | null | As large language models have demonstrated impressive performance in many domains, recent works have adopted language models (LMs) as controllers of visual modules for vision-and-language tasks. While existing work focuses on equipping LMs with visual understanding, we propose two novel interpretable/explainable visual programming frameworks for text-to-image (T2I) generation and evaluation. First, we introduce VPGen, an interpretable step-by-step T2I generation framework that decomposes T2I generation into three steps: object/count generation, layout generation, and image generation. We employ an LM to handle the first two steps (object/count generation and layout generation), by finetuning it on text-layout pairs. Our step-by-step T2I generation framework provides stronger spatial control than end-to-end models, the dominant approach for this task. Furthermore, we leverage the world knowledge of pretrained LMs, overcoming the limitation of previous layout-guided T2I works that can only handle predefined object classes. We demonstrate that our VPGen has improved control in counts/spatial relations/scales of objects than state-of-the-art T2I generation models. Second, we introduce VPEval, an interpretable and explainable evaluation framework for T2I generation based on visual programming. Unlike previous T2I evaluations with a single scoring model that is accurate in some skills but unreliable in others, VPEval produces evaluation programs that invoke a set of visual modules that are experts in different skills, and also provides visual+textual explanations of the evaluation results. Our analysis shows that VPEval provides a more human-correlated evaluation for skill-specific and open-ended prompts than widely used single model-based evaluation. We hope that our work encourages future progress on interpretable/explainable generation and evaluation for T2I models. | null |
Auditing Fairness by Betting | https://papers.nips.cc/paper_files/paper/2023/hash/1338c277525011f20166cf740952bb47-Abstract-Conference.html | Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder, Aaditya Ramdas | https://papers.nips.cc/paper_files/paper/2023/hash/1338c277525011f20166cf740952bb47-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21104-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1338c277525011f20166cf740952bb47-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1338c277525011f20166cf740952bb47-Supplemental-Conference.zip | We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sample size, our methods are sequential and allow for the continuous monitoring of incoming data, making them highly amenable to tracking the fairness of real-world systems. We also allow the data to be collected by a probabilistic policy as opposed to sampled uniformly from the population. This enables auditing to be conducted on data gathered for another purpose. Moreover, this policy may change over time and different policies may be used on different subpopulations. Finally, our methods can handle distribution shift resulting from either changes to the model or changes in the underlying population. Our approach is based on recent progress in anytime-valid inference and game-theoretic statistics---the ``testing by betting'' framework in particular. These connections ensure that our methods are interpretable, fast, and easy to implement. We demonstrate the efficacy of our approach on three benchmark fairness datasets. | null |
Truly Scale-Equivariant Deep Nets with Fourier Layers | https://papers.nips.cc/paper_files/paper/2023/hash/1343edb2739a61a6e20bd8764e814b50-Abstract-Conference.html | Md Ashiqur Rahman, Raymond A. Yeh | https://papers.nips.cc/paper_files/paper/2023/hash/1343edb2739a61a6e20bd8764e814b50-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22742-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1343edb2739a61a6e20bd8764e814b50-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1343edb2739a61a6e20bd8764e814b50-Supplemental-Conference.pdf | In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance. Recent works have made progress in developing scale-equivariant convolutional neural networks, e.g., through weight-sharing and kernel resizing. However, these networks are not truly scale-equivariant in practice. Specifically, they do not consider anti-aliasing as they formulate the down-scaling operation in the continuous domain. To address this shortcoming, we directly formulate down-scaling in the discrete domain with consideration of anti-aliasing. We then propose a novel architecture based on Fourier layers to achieve truly scale-equivariant deep nets, i.e., absolute zero equivariance-error. Following prior works, we test this model on MNIST-scale and STL-10 datasets. Our proposed model achieves competitive classification performance while maintaining zero equivariance-error. | null |
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem | https://papers.nips.cc/paper_files/paper/2023/hash/136729ae4b0fee25a0d28077442506da-Abstract-Conference.html | Jincheng Cao, Ruichen Jiang, Nazanin Abolfazli, Erfan Yazdandoost Hamedani, Aryan Mokhtari | https://papers.nips.cc/paper_files/paper/2023/hash/136729ae4b0fee25a0d28077442506da-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19779-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/136729ae4b0fee25a0d28077442506da-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/136729ae4b0fee25a0d28077442506da-Supplemental-Conference.zip | In this paper, we study a class of stochastic bilevel optimization problems, also known as stochastic simple bilevel optimization, where we minimize a smooth stochastic objective function over the optimal solution set of another stochastic convex optimization problem. We introduce novel stochastic bilevel optimization methods that locally approximate the solution set of the lower-level problem via a stochastic cutting plane, and then run a conditional gradient update with variance reduction techniques to control the error induced by using stochastic gradients. For the case that the upper-level function is convex, our method requires $\mathcal{O}(\max\\{1/\epsilon_f^{2},1/\epsilon_g^{2}\\}) $ stochastic oracle queries to obtain a solution that is $\epsilon_f$-optimal for the upper-level and $\epsilon_g$-optimal for the lower-level. This guarantee improves the previous best-known complexity of $\mathcal{O}(\max\\{1/\epsilon_f^{4},1/\epsilon_g^{4}\\})$. Moreover, for the case that the upper-level function is non-convex, our method requires at most $\mathcal{O}(\max\\{1/\epsilon_f^{3},1/\epsilon_g^{3}\\}) $ stochastic oracle queries to find an $(\epsilon_f, \epsilon_g)$-stationary point. In the finite-sum setting, we show that the number of stochastic oracle calls required by our method are $\mathcal{O}(\sqrt{n}/\epsilon)$ and $\mathcal{O}(\sqrt{n}/\epsilon^{2})$ for the convex and non-convex settings, respectively, where $\epsilon=\min \\{\epsilon_f,\epsilon_g\\}$. | null |
On the Implicit Bias of Linear Equivariant Steerable Networks | https://papers.nips.cc/paper_files/paper/2023/hash/136a45cd9b841bf785625709a19c6508-Abstract-Conference.html | Ziyu Chen, Wei Zhu | https://papers.nips.cc/paper_files/paper/2023/hash/136a45cd9b841bf785625709a19c6508-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22553-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/136a45cd9b841bf785625709a19c6508-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/136a45cd9b841bf785625709a19c6508-Supplemental-Conference.pdf | We study the implicit bias of gradient flow on linear equivariant steerable networks in group-invariant binary classification. Our findings reveal that the parameterized predictor converges in direction to the unique group-invariant classifier with a maximum margin defined by the input group action. Under a unitary assumption on the input representation, we establish the equivalence between steerable networks and data augmentation. Furthermore, we demonstrate the improved margin and generalization bound of steerable networks over their non-invariant counterparts. | null |
Memory-Constrained Algorithms for Convex Optimization | https://papers.nips.cc/paper_files/paper/2023/hash/1395b425d06a50e42fafe91cf04f3a98-Abstract-Conference.html | Moise Blanchard, Junhui Zhang, Patrick Jaillet | https://papers.nips.cc/paper_files/paper/2023/hash/1395b425d06a50e42fafe91cf04f3a98-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19481-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1395b425d06a50e42fafe91cf04f3a98-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1395b425d06a50e42fafe91cf04f3a98-Supplemental-Conference.pdf | We propose a family of recursive cutting-plane algorithms to solve feasibility problems with constrained memory, which can also be used for first-order convex optimization. Precisely, in order to find a point within a ball of radius $\epsilon$ with a separation oracle in dimension $d$---or to minimize $1$-Lipschitz convex functions to accuracy $\epsilon$ over the unit ball---our algorithms use $\mathcal O(\frac{d^2}{p}\ln \frac{1}{\epsilon})$ bits of memory, and make $\mathcal O((C\frac{d}{p}\ln \frac{1}{\epsilon})^p)$ oracle calls. The family is parametrized by $p\in[d]$ and provides an oracle-complexity/memory trade-off in the sub-polynomial regime $\ln\frac{1}{\epsilon}\gg\ln d$. While several works gave lower-bound trade-offs (impossibility results)---we explicit here their dependence with $\ln\frac{1}{\epsilon}$, showing that these also hold in any sub-polynomial regime---to the best of our knowledge this is the first class of algorithms that provides a positive trade-off between gradient descent and cutting-plane methods in any regime with $\epsilon\leq 1/\sqrt d$. The algorithms divide the $d$ variables into $p$ blocks and optimize over blocks sequentially, with approximate separation vectors constructed using a variant of Vaidya's method. In the regime $\epsilon \leq d^{-\Omega(d)}$, our algorithm with $p=d$ achieves the information-theoretic optimal memory usage and improves the oracle-complexity of gradient descent. | null |
Nonparametric Boundary Geometry in Physics Informed Deep Learning | https://papers.nips.cc/paper_files/paper/2023/hash/13aef57cf532e88c476a10ff372e44e5-Abstract-Conference.html | Scott Cameron, Arnu Pretorius, S Roberts | https://papers.nips.cc/paper_files/paper/2023/hash/13aef57cf532e88c476a10ff372e44e5-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21403-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/13aef57cf532e88c476a10ff372e44e5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/13aef57cf532e88c476a10ff372e44e5-Supplemental-Conference.zip | Engineering design problems frequently require solving systems ofpartial differential equations with boundary conditions specified onobject geometries in the form of a triangular mesh. These boundarygeometries are provided by a designer and are problem dependent.The efficiency of the design process greatly benefits from fast turnaroundtimes when repeatedly solving PDEs on various geometries. However,most current work that uses machine learning to speed up the solutionprocess relies heavily on a fixed parameterization of the geometry, whichcannot be changed after training. This severely limits the possibility ofreusing a trained model across a variety of design problems.In this work, we propose a novel neural operator architecture which acceptsboundary geometry, in the form of triangular meshes, as input and produces anapproximate solution to a given PDE as output. Once trained, the model can beused to rapidly estimate the PDE solution over a new geometry, without the need forretraining or representation of the geometry to a pre-specified parameterization. | null |
Tracking Most Significant Shifts in Nonparametric Contextual Bandits | https://papers.nips.cc/paper_files/paper/2023/hash/13b501c58ae3bfe9635a259f4414e943-Abstract-Conference.html | Joe Suk, Samory Kpotufe | https://papers.nips.cc/paper_files/paper/2023/hash/13b501c58ae3bfe9635a259f4414e943-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21797-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/13b501c58ae3bfe9635a259f4414e943-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/13b501c58ae3bfe9635a259f4414e943-Supplemental-Conference.pdf | We study nonparametric contextual bandits where Lipschitz mean reward functions may change over time.We first establish the minimax dynamic regret rate in this less understood setting in terms of number of changes $L$ and total-variation $V$, both capturing all changes in distribution over context space, and argue that state-of-the-art procedures are suboptimal in this setting.Next, we tend to the question of an _adaptivity_ for this setting, i.e. achieving the minimax rate without knowledge of $L$ or $V$. Quite importantly, we posit that the bandit problem, viewed locally at a given context $X_t$, should not be affected by reward changes in other parts of context space $\cal X$. We therefore propose a notion of _change_, which we term _experienced significant shifts_, that better accounts for locality, and thus counts considerably less changes than $L$ and $V$. Furthermore, similar to recent work on non-stationary MAB (Suk & Kpotufe, 2022), _experienced significant shifts_ only count the most _significant_ changes in mean rewards, e.g., severe best-arm changes relevant to observed contexts.Our main result is to show that this more tolerant notion of change can in fact be adapted to. | null |
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss | https://papers.nips.cc/paper_files/paper/2023/hash/13f1750b825659394a6499399e7637fc-Abstract-Conference.html | An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang, Tat-Seng Chua | https://papers.nips.cc/paper_files/paper/2023/hash/13f1750b825659394a6499399e7637fc-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20159-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/13f1750b825659394a6499399e7637fc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/13f1750b825659394a6499399e7637fc-Supplemental-Conference.pdf | Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevailing in semi-supervised topK recommendations. The basic idea is to routinely conduct heuristic-based data augmentation and apply contrastive losses (e.g., InfoNCE) on the augmented views. Yet, some CF-tailored challenges make this adoption suboptimal, such as the issue of out-of-distribution, the risk of false negatives, and the nature of top-K evaluation. They necessitate the CL-based CF scheme to focus more on mining hard negatives and distinguishing false negatives from the vast unlabeled user-item interactions, for informative contrast signals. Worse still, there is limited understanding of contrastive loss in CF methods, especially w.r.t. its generalization ability. To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods. AdvInfoNCE adaptively explores and assigns hardness to each negative instance in an adversarial fashion and further utilizes a fine-grained hardness-aware ranking criterion to empower the recommender’s generalization ability. Training CF models with AdvInfoNCE, we validate the effectiveness of AdvInfoNCE on both synthetic and real-world benchmark datasets, thus showing its generalization ability to mitigate out-of-distribution problems. Given the theoretical guarantees and empirical superiority of AdvInfoNCE over most contrastive loss functions, we advocate its adoption as a standard loss in recommender systems, particularly for the out-of-distribution tasks. Codes are available at https://github.com/LehengTHU/AdvInfoNCE. | null |
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance | https://papers.nips.cc/paper_files/paper/2023/hash/1403ab1a427050538ec59c7f570aec8b-Abstract-Conference.html | Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward Browne | https://papers.nips.cc/paper_files/paper/2023/hash/1403ab1a427050538ec59c7f570aec8b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20230-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1403ab1a427050538ec59c7f570aec8b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1403ab1a427050538ec59c7f570aec8b-Supplemental-Conference.pdf | Quantifying variable importance is essential for answering high-stakes questions in fields like genetics, public policy, and medicine. Current methods generally calculate variable importance for a given model trained on a given dataset. However, for a given dataset, there may be many models that explain the target outcome equally well; without accounting for all possible explanations, different researchers may arrive at many conflicting yet equally valid conclusions given the same data. Additionally, even when accounting for all possible explanations for a given dataset, these insights may not generalize because not all good explanations are stable across reasonable data perturbations. We propose a new variable importance framework that quantifies the importance of a variable across the set of all good models and is stable across the data distribution. Our framework is extremely flexible and can be integrated with most existing model classes and global variable importance metrics. We demonstrate through experiments that our framework recovers variable importance rankings for complex simulation setups where other methods fail. Further, we show that our framework accurately estimates the true importance of a variable for the underlying data distribution. We provide theoretical guarantees on the consistency and finite sample error rates for our estimator. Finally, we demonstrate its utility with a real-world case study exploring which genes are important for predicting HIV load in persons with HIV, highlighting an important gene that has not previously been studied in connection with HIV. | null |
Model-Based Control with Sparse Neural Dynamics | https://papers.nips.cc/paper_files/paper/2023/hash/142cdba4b8d1e03f9ee131ac86bb0afc-Abstract-Conference.html | Ziang Liu, Genggeng Zhou, Jeff He, Tobia Marcucci, Fei-Fei Li, Jiajun Wu, Yunzhu Li | https://papers.nips.cc/paper_files/paper/2023/hash/142cdba4b8d1e03f9ee131ac86bb0afc-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20781-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/142cdba4b8d1e03f9ee131ac86bb0afc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/142cdba4b8d1e03f9ee131ac86bb0afc-Supplemental-Conference.zip | Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs are too unstructured for effective planning, and current control methods typically rely on extensive sampling or local gradient descent. In this paper, we propose a new framework for integrated model learning and predictive control that is amenable to efficient optimization algorithms. Specifically, we start with a ReLU neural model of the system dynamics and, with minimal losses in prediction accuracy, we gradually sparsify it by removing redundant neurons. This discrete sparsification process is approximated as a continuous problem, enabling an end-to-end optimization of both the model architecture and the weight parameters. The sparsified model is subsequently used by a mixed-integer predictive controller, which represents the neuron activations as binary variables and employs efficient branch-and-bound algorithms. Our framework is applicable to a wide variety of DNNs, from simple multilayer perceptrons to complex graph neural dynamics. It can efficiently handle tasks involving complicated contact dynamics, such as object pushing, compositional object sorting, and manipulation of deformable objects. Numerical and hardware experiments show that, despite the aggressive sparsification, our framework can deliver better closed-loop performance than existing state-of-the-art methods. | null |
AmadeusGPT: a natural language interface for interactive animal behavioral analysis | https://papers.nips.cc/paper_files/paper/2023/hash/1456560769bbc38e4f8c5055048ea712-Abstract-Conference.html | Shaokai Ye, Jessy Lauer, Mu Zhou, Alexander Mathis, Mackenzie Mathis | https://papers.nips.cc/paper_files/paper/2023/hash/1456560769bbc38e4f8c5055048ea712-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19972-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1456560769bbc38e4f8c5055048ea712-Paper-Conference.pdf | null | The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of animal behavior and technical machine learning knowledge. To limit this gap, we introduce AmadeusGPT: a natural language interface that turns natural language descriptions of behaviors into machine-executable code. Large-language models (LLMs) such as GPT3.5 and GPT4 allow for interactive language-based queries that are potentially well suited for making interactive behavior analysis. However, the comprehension capability of these LLMs is limited by the context window size, which prevents it from remembering distant conversations. To overcome the context window limitation, we implement a novel dual-memory mechanism to allow communication between short-term and long-term memory using symbols as context pointers for retrieval and saving. Concretely, users directly use language-based definitions of behavior and our augmented GPT develops code based on the core AmadeusGPT API, which contains machine learning, computer vision, spatio-temporal reasoning, and visualization modules. Users then can interactively refine results, and seamlessly add new behavioral modules as needed. We used the MABe 2022 behavior challenge tasks to benchmark AmadeusGPT and show excellent performance. Note, an end-user would not need to write any code to achieve this. Thus, collectively AmadeusGPT presents a novel way to merge deep biological knowledge, large-language models, and core computer vision modules into a more naturally intelligent system. Code and demos can be found at: https://github.com/AdaptiveMotorControlLab/AmadeusGPT | null |
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs | https://papers.nips.cc/paper_files/paper/2023/hash/145c28cd4b1df9b426990fd68045f4f7-Abstract-Conference.html | Yuan Cheng, Jing Yang, Yingbin Liang | https://papers.nips.cc/paper_files/paper/2023/hash/145c28cd4b1df9b426990fd68045f4f7-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20371-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/145c28cd4b1df9b426990fd68045f4f7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/145c28cd4b1df9b426990fd68045f4f7-Supplemental-Conference.pdf | Reinforcement learning (RL) under changing environment models many real-world applications via nonstationary Markov Decision Processes (MDPs), and hence gains considerable interest. However, theoretical studies on nonstationary MDPs in the literature have mainly focused on tabular and linear (mixture) MDPs, which do not capture the nature of unknown representation in deep RL. In this paper, we make the first effort to investigate nonstationary RL under episodic low-rank MDPs, where both transition kernels and rewards may vary over time, and the low-rank model contains unknown representation in addition to the linear state embedding function. We first propose a parameter-dependent policy optimization algorithm called PORTAL,and further improve PORTAL to its parameter-free version of Ada-PORTAL, which is able to tune its hyper-parameters adaptively without any prior knowledge of nonstationarity. For both algorithms, we provide upper bounds on the average dynamic suboptimality gap, which show that as long as the nonstationarity is not significantly large, PORTAL and Ada-PORTAL are sample-efficient and can achieve arbitrarily small average dynamic suboptimality gap with polynomial sample complexity. | null |
Time-uniform confidence bands for the CDF under nonstationarity | https://papers.nips.cc/paper_files/paper/2023/hash/148bbc25b934211d80435b5cad5a7198-Abstract-Conference.html | Paul Mineiro, Steven Howard | https://papers.nips.cc/paper_files/paper/2023/hash/148bbc25b934211d80435b5cad5a7198-Abstract-Conference.html | NIPS 2023 | null | null | null | null | null |
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure | https://papers.nips.cc/paper_files/paper/2023/hash/1498a03a04f9bcd3a7d44058fc5dc639-Abstract-Conference.html | Yuchao Qin, Mihaela van der Schaar, Changhee Lee | https://papers.nips.cc/paper_files/paper/2023/hash/1498a03a04f9bcd3a7d44058fc5dc639-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20432-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1498a03a04f9bcd3a7d44058fc5dc639-Paper-Conference.pdf | null | Timely outcome prediction is essential in healthcare to enable early detection and intervention of adverse events. However, in longitudinal follow-ups to patients' health status, cost-efficient acquisition of patient covariates is usually necessary due to the significant expense involved in screening and lab tests. To balance the timely and accurate outcome predictions with acquisition costs, an effective active sensing strategy is crucial. In this paper, we propose a novel risk-averse active sensing approach RAS that addresses the composite decision problem of when to conduct the acquisition and which measurements to make. Our approach decomposes the policy into two sub-policies: acquisition scheduler and feature selector, respectively. Moreover, we introduce a novel risk-aversion training strategy to focus on the underrepresented subgroup of high-risk patients for whom timely and accurate prediction of disease progression is of greater value. Our method outperforms baseline active sensing approaches in experiments with both synthetic and real-world datasets, and we illustrate the significance of our policy decomposition and the necessity of a risk-averse sensing policy through case studies. | null |
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple! | https://papers.nips.cc/paper_files/paper/2023/hash/149ad6e32c08b73a3ecc3d11977fcc47-Abstract-Conference.html | Konstantin Makarychev, Sayak Chakrabarty | https://papers.nips.cc/paper_files/paper/2023/hash/149ad6e32c08b73a3ecc3d11977fcc47-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22992-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/149ad6e32c08b73a3ecc3d11977fcc47-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/149ad6e32c08b73a3ecc3d11977fcc47-Supplemental-Conference.zip | We show that a simple single-pass semi-streaming variant of the Pivot algorithm for Correlation Clustering gives a (3+eps)-approximation using O(n/eps) words of memory. This is a slight improvement over the recent results of Cambus, Kuhn, Lindy, Pai, and Uitto, who gave a (3+eps)-approximation using O(n log n) words of memory, and Behnezhad, Charikar, Ma, and Tan, who gave a 5-approximation using O(n) words of memory. One of the main contributions of our paper is that the algorithm and its analysis are simple and easy to understand. | null |
SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | https://papers.nips.cc/paper_files/paper/2023/hash/14a812fa4b6bf244d055e37a7cd2f557-Abstract-Conference.html | Yi-Chung Chen, Hsi-Wen Chen, Shun-Gui Wang, Ming-syan Chen | https://papers.nips.cc/paper_files/paper/2023/hash/14a812fa4b6bf244d055e37a7cd2f557-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21428-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/14a812fa4b6bf244d055e37a7cd2f557-Paper-Conference.pdf | null | The evaluation of participant contribution in federated learning (FL) has recently gained significant attention due to its applicability in various domains, such as incentive mechanisms, robustness enhancement, and client selection. Previous approaches have predominantly relied on the widely adopted Shapley value for participant evaluation. However, the computation of the Shapley value is expensive, despite using techniques like gradient-based model reconstruction and truncating unnecessary evaluations. Therefore, we present an efficient approach called Single-round Participants Amalgamation for Contribution Evaluation (SPACE). SPACE incorporates two novel components, namely Federated Knowledge Amalgamation and Prototype-based Model Evaluation to reduce the evaluation effort by eliminating the dependence on the size of the validation set and enabling participant evaluation within a single communication round. Experimental results demonstrate that SPACE outperforms state-of-the-art methods in terms of both running time and Pearson’s Correlation Coefficient (PCC). Furthermore, extensive experiments conducted on applications, client reweighting, and client selection highlight the effectiveness of SPACE. The code is available at https://github.com/culiver/SPACE. | null |
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations | https://papers.nips.cc/paper_files/paper/2023/hash/14cdc9013d80338bf81483a7736ea05c-Abstract-Conference.html | Ziyuan Ye, Rihan Huang, Qilin Wu, Quanying Liu | https://papers.nips.cc/paper_files/paper/2023/hash/14cdc9013d80338bf81483a7736ea05c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19635-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/14cdc9013d80338bf81483a7736ea05c-Paper-Conference.pdf | null | Post-hoc explanation techniques on graph neural networks (GNNs) provide economical solutions for opening the black-box graph models without model retraining. Many GNN explanation variants have achieved state-of-the-art explaining results on a diverse set of benchmarks, while they rarely provide theoretical analysis for their inherent properties and explanatory capability. In this work, we propose $\underline{\text{S}}$tructure-$\underline{\text{A}}$ware Shapley-based $\underline{\text{M}}$ultipiece $\underline{\text{E}}$xplanation (SAME) method to address the structure-aware feature interactions challenges for GNNs explanation. Specifically, SAME leverages an expansion-based Monte Carlo tree search to explore the multi-grained structure-aware connected substructure. Afterward, the explanation results are encouraged to be informative of the graph properties by optimizing the combination of distinct single substructures. With the consideration of fair feature interactions in the process of investigating multiple connected important substructures, the explanation provided by SAME has the potential to be as explainable as the theoretically optimal explanation obtained by the Shapley value within polynomial time. Extensive experiments on real-world and synthetic benchmarks show that SAME improves the previous state-of-the-art fidelity performance by 12.9\% on BBBP, 7.01\% on MUTAG, 42.3\% on Graph-SST2, 38.9\% on Graph-SST5, 11.3\% on BA-2Motifs and 18.2\% on BA-Shapes under the same testing condition. Code is available at https://github.com/same2023neurips/same. | null |
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | https://papers.nips.cc/paper_files/paper/2023/hash/14ecbfb2216bab76195b60bfac7efb1f-Abstract-Conference.html | Michael Crawshaw, Yajie Bao, Mingrui Liu | https://papers.nips.cc/paper_files/paper/2023/hash/14ecbfb2216bab76195b60bfac7efb1f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22460-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/14ecbfb2216bab76195b60bfac7efb1f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/14ecbfb2216bab76195b60bfac7efb1f-Supplemental-Conference.zip | We study the problem of Federated Learning (FL) under client subsampling and data heterogeneity with an objective function that has potentially unbounded smoothness. This problem is motivated by empirical evidence that the class of relaxed smooth functions, where the Lipschitz constant of the gradient scales linearly with the gradient norm, closely resembles the loss functions of certain neural networks such as recurrent neural networks (RNNs) with possibly exploding gradient. We introduce EPISODE++, the first algorithm to solve this problem. It maintains historical statistics for each client to construct control variates and decide clipping behavior for sampled clients in the current round. We prove that EPISODE++ achieves linear speedup in the number of participating clients, reduced communication rounds, and resilience to data heterogeneity. Our upper bound proof relies on novel techniques of recursively bounding the client updates under unbounded smoothness and client subsampling, together with a refined high probability analysis. In addition, we prove a lower bound showing that the convergence rate of a special case of clipped minibatch SGD (without randomness in the stochastic gradient and with randomness in client subsampling) suffers from an explicit dependence on the maximum gradient norm of the objective in a sublevel set, which may be large. This effectively demonstrates that applying gradient clipping to minibatch SGD in our setting does not eliminate the problem of exploding gradients. Our lower bound is based on new constructions of hard instances tailored to client subsampling and a novel analysis of the trajectory of the algorithm in the presence of clipping. Lastly, we provide an experimental evaluation of EPISODE++ when training RNNs on federated text classification tasks, demonstrating that EPISODE++ outperforms strong baselines in FL. The code is available at https://github.com/MingruiLiu-ML-Lab/episode_plusplus. | null |
Quantifying the Cost of Learning in Queueing Systems | https://papers.nips.cc/paper_files/paper/2023/hash/1502957929fc4257dd1b6daf7d869c2f-Abstract-Conference.html | Daniel Freund, Thodoris Lykouris, Wentao Weng | https://papers.nips.cc/paper_files/paper/2023/hash/1502957929fc4257dd1b6daf7d869c2f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22861-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1502957929fc4257dd1b6daf7d869c2f-Paper-Conference.pdf | null | Queueing systems are widely applicable stochastic models with use cases in communication networks, healthcare, service systems, etc. Although their optimal control has been extensively studied, most existing approaches assume perfect knowledge of the system parameters. Of course, this assumption rarely holds in practice where there is parameter uncertainty, thus motivating a recent line of work on bandit learning for queueing systems. This nascent stream of research focuses on the asymptotic performance of the proposed algorithms. In this paper, we argue that an asymptotic metric, which focuses on late-stage performance, is insufficient to capture the intrinsic statistical complexity of learning in queueing systems which typically occurs in the early stage. Instead, we propose the Cost of Learning in Queueing (CLQ), a new metric that quantifies the maximum increase in time-averaged queue length caused by parameter uncertainty.We characterize the CLQ of a single-queue multi-server system, and then extend these results to multi-queue multi-server systems and networks of queues. In establishing our results, we propose a unified analysis framework for CLQ that bridges Lyapunov and bandit analysis, provides guarantees for a wide range of algorithms, and could be of independent interest. | null |
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models | https://papers.nips.cc/paper_files/paper/2023/hash/1516a7f7507d5550db5c7f29e995ec8c-Abstract-Conference.html | Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone | https://papers.nips.cc/paper_files/paper/2023/hash/1516a7f7507d5550db5c7f29e995ec8c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22311-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1516a7f7507d5550db5c7f29e995ec8c-Paper-Conference.pdf | null | Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. However, they typically achieve lower sample quality compared to state-of-the-art score-based Diffusion Models (DMs). This paper provides a significant step in the direction of addressing this limitation. The idea is to borrow one of the strengths of score-based DMs, which is the ability to perform accurate density estimation in low-density regions and to address manifold overfitting by means of data mollification. We propose a view of data mollification within likelihood-based GMs as a continuation method, whereby the optimization objective smoothly transitions from simple-to-optimize to the original target. Crucially, data mollification can be implemented by adding one line of code in the optimization loop, and we demonstrate that this provides a boost in generation quality of likelihood-based GMs, without computational overheads. We report results on real-world image data sets and UCI benchmarks with popular likelihood-based GMs, including variants of variational autoencoders and normalizing flows, showing large improvements in FID score and density estimation. | null |
FLSL: Feature-level Self-supervised Learning | https://papers.nips.cc/paper_files/paper/2023/hash/15212bd2265c4a3ab0dbc1b1982c1b69-Abstract-Conference.html | Qing Su, Anton Netchaev, Hai Li, Shihao Ji | https://papers.nips.cc/paper_files/paper/2023/hash/15212bd2265c4a3ab0dbc1b1982c1b69-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21181-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15212bd2265c4a3ab0dbc1b1982c1b69-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15212bd2265c4a3ab0dbc1b1982c1b69-Supplemental-Conference.pdf | Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg, MOCOv3) target primarily on representations at instance level and do not generalize well to dense prediction tasks, such as object detection and segmentation. Towards aligning SSL with dense predictions, this paper demonstrates for the first time the underlying mean-shift clustering process of Vision Transformers (ViT), which aligns well with natural image semantics (e.g., a world of objects and stuffs). By employing transformer for joint embedding and clustering, we propose a bi-level feature clustering SSL method, coined Feature-Level Self-supervised Learning (FLSL). We present the formal definition of the FLSL problem and construct the objectives from the mean-shift and k-means perspectives. We show that FLSL promotes remarkable semantic cluster representations and learns an embedding scheme amenable to intra-view and inter-view feature clustering. Experiments show that FLSL yields significant improvements in dense prediction tasks, achieving 44.9 (+2.8)% AP and 46.5% AP in object detection, as well as 40.8 (+2.3)% AP and 42.1% AP in instance segmentation on MS-COCO, using Mask R-CNN with ViT-S/16 and ViT-S/8 as backbone, respectively. FLSL consistently outperforms existing SSL methods across additional benchmarks, including UAV object detection on UAVDT, and video instance segmentation on DAVIS 2017. We conclude by presenting visualization and various ablation studies to better understand the success of FLSL. The source code is available at https://github.com/ISL-CV/FLSL. | null |
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning | https://papers.nips.cc/paper_files/paper/2023/hash/15294ba2dcfb4521274f7aa1c26f4dd4-Abstract-Conference.html | Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski, Joost van de Weijer | https://papers.nips.cc/paper_files/paper/2023/hash/15294ba2dcfb4521274f7aa1c26f4dd4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19556-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15294ba2dcfb4521274f7aa1c26f4dd4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15294ba2dcfb4521274f7aa1c26f4dd4-Supplemental-Conference.pdf | Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In this paper, we explore prototypical networks for CIL, which generate new class prototypes using the frozen feature extractor and classify the features based on the Euclidean distance to the prototypes. In an analysis of the feature distributions of classes, we show that classification based on Euclidean metrics is successful for jointly trained features. However, when learning from non-stationary data, we observe that the Euclidean metric is suboptimal and that feature distributions are heterogeneous. To address this challenge, we revisit the anisotropic Mahalanobis distance for CIL. In addition, we empirically show that modeling the feature covariance relations is better than previous attempts at sampling features from normal distributions and training a linear classifier. Unlike existing methods, our approach generalizes to both many- and few-shot CIL settings, as well as to domain-incremental settings. Interestingly, without updating the backbone network, our method obtains state-of-the-art results on several standard continual learning benchmarks. Code is available at https://github.com/dipamgoswami/FeCAM. | null |
Learning non-Markovian Decision-Making from State-only Sequences | https://papers.nips.cc/paper_files/paper/2023/hash/154926e0b66e2b2a8c1120852f31a12d-Abstract-Conference.html | Aoyang Qin, Feng Gao, Qing Li, Song-Chun Zhu, Sirui Xie | https://papers.nips.cc/paper_files/paper/2023/hash/154926e0b66e2b2a8c1120852f31a12d-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22856-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/154926e0b66e2b2a8c1120852f31a12d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/154926e0b66e2b2a8c1120852f31a12d-Supplemental-Conference.pdf | Conventional imitation learning assumes access to the actions of demonstrators, but these motor signals are often non-observable in naturalistic settings. Additionally, sequential decision-making behaviors in these settings can deviate from the assumptions of a standard Markov Decision Process (MDP). To address these challenges, we explore deep generative modeling of state-only sequences with non-Markov Decision Process (nMDP), where the policy is an energy-based prior in the latent space of the state transition generator. We develop maximum likelihood estimation to achieve model-based imitation, which involves short-run MCMC sampling from the prior and importance sampling for the posterior. The learned model enables $\textit{decision-making as inference}$: model-free policy execution is equivalent to prior sampling, model-based planning is posterior sampling initialized from the policy. We demonstrate the efficacy of the proposed method in a prototypical path planning task with non-Markovian constraints and show that the learned model exhibits strong performances in challenging domains from the MuJoCo suite. | null |
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts | https://papers.nips.cc/paper_files/paper/2023/hash/154b90fcc9ba3dee96779c05c3108908-Abstract-Conference.html | Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue', Haoyang Li, Wenwu Zhu | https://papers.nips.cc/paper_files/paper/2023/hash/154b90fcc9ba3dee96779c05c3108908-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19486-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/154b90fcc9ba3dee96779c05c3108908-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/154b90fcc9ba3dee96779c05c3108908-Supplemental-Conference.pdf | Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs.Existing work on DyGNNs with out-of-distribution settings only focuses on the time domain, failing to handle cases involving distribution shifts in the spectral domain. In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.However, this investigation poses two key challenges: i) it is non-trivial to capture different graph patterns that are driven by various frequency components entangled in the spectral domain; and ii) it remains unclear how to handle distribution shifts with the discovered spectral patterns. To address these challenges, we propose Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts (SILD), which can handle distribution shifts on dynamic graphs by capturing and utilizing invariant and variant spectral patterns. Specifically, we first design a DyGNN with Fourier transform to obtain the ego-graph trajectory spectrums, allowing the mixed dynamic graph patterns to be transformed into separate frequency components. We then develop a disentangled spectrum mask to filter graph dynamics from various frequency components and discover the invariant and variant spectral patterns. Finally, we propose invariant spectral filtering, which encourages the model to rely on invariant patterns for generalization under distribution shifts. Experimental results on synthetic and real-world dynamic graph datasets demonstrate the superiority of our method for both node classification and link prediction tasks under distribution shifts. | null |
Efficient Activation Function Optimization through Surrogate Modeling | https://papers.nips.cc/paper_files/paper/2023/hash/154d63285d3ed7826e7f026c0b350d69-Abstract-Conference.html | Garrett Bingham, Risto Miikkulainen | https://papers.nips.cc/paper_files/paper/2023/hash/154d63285d3ed7826e7f026c0b350d69-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19860-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/154d63285d3ed7826e7f026c0b350d69-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/154d63285d3ed7826e7f026c0b350d69-Supplemental-Conference.zip | Carefully designed activation functions can improve the performance of neural networks in many machine learning tasks. However, it is difficult for humans to construct optimal activation functions, and current activation function search algorithms are prohibitively expensive. This paper aims to improve the state of the art through three steps: First, the benchmark datasets Act-Bench-CNN, Act-Bench-ResNet, and Act-Bench-ViT were created by training convolutional, residual, and vision transformer architectures from scratch with 2,913 systematically generated activation functions. Second, a characterization of the benchmark space was developed, leading to a new surrogate-based method for optimization. More specifically, the spectrum of the Fisher information matrix associated with the model's predictive distribution at initialization and the activation function's output distribution were found to be highly predictive of performance. Third, the surrogate was used to discover improved activation functions in several real-world tasks, with a surprising finding: a sigmoidal design that outperformed all other activation functions was discovered, challenging the status quo of always using rectifier nonlinearities in deep learning. Each of these steps is a contribution in its own right; together they serve as a practical and theoretical foundation for further research on activation function optimization. | null |
Data Market Design through Deep Learning | https://papers.nips.cc/paper_files/paper/2023/hash/1577ea3eaf8dacb99f64e4496c3ecddf-Abstract-Conference.html | Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes | https://papers.nips.cc/paper_files/paper/2023/hash/1577ea3eaf8dacb99f64e4496c3ecddf-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20547-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1577ea3eaf8dacb99f64e4496c3ecddf-Paper-Conference.pdf | null | The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price. Each buyer has their own decision to make in a world environment, and their subjective expected value for the information associated with a particular experiment comes from the improvement in this decision and depends on their prior and value for different outcomes. In a setting with multiple buyers, a buyer's expected value for an experiment may also depend on the information sold to others. We introduce the application of deep learning for the design of revenue-optimal data markets, looking to expand the frontiers of what can be understood and achieved. Relative to earlier work on deep learning for auction design, we must learn signaling schemes rather than allocation rules and handle obedience constraints — these arising from modeling the downstream actions of buyers — in addition to incentive constraints on bids. Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs. | null |
When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation | https://papers.nips.cc/paper_files/paper/2023/hash/157c30da6a988e1cbef2095f7b9521db-Abstract-Conference.html | Xinhong Ma, Yiming Wang, Hao Liu, Tianyu Guo, Yunhe Wang | https://papers.nips.cc/paper_files/paper/2023/hash/157c30da6a988e1cbef2095f7b9521db-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20131-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/157c30da6a988e1cbef2095f7b9521db-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/157c30da6a988e1cbef2095f7b9521db-Supplemental-Conference.pdf | Source-free domain adaptive semantic segmentation aims to adapt a pre-trained source model to the unlabeled target domain without accessing the private source data. Previous methods usually fine-tune the entire network, which suffers from expensive parameter tuning. To avoid this problem, we propose to utilize visual prompt tuning for parameter-efficient adaptation. However, the existing visual prompt tuning methods are unsuitable for source-free domain adaptive semantic segmentation due to the following two reasons: (1) Commonly used visual prompts like input tokens or pixel-level perturbations cannot reliably learn informative knowledge beneficial for semantic segmentation. (2) Visual prompts require sufficient labeled data to fill the gap between the pre-trained model and downstream tasks. To alleviate these problems, we propose a universal unsupervised visual prompt tuning (Uni-UVPT) framework, which is applicable to various transformer-based backbones. Specifically, we first divide the source pre-trained backbone with frozen parameters into multiple stages, and propose a lightweight prompt adapter for progressively encoding informative knowledge into prompts and enhancing the generalization of target features between adjacent backbone stages. Cooperatively, a novel adaptive pseudo-label correction strategy with a multiscale consistency loss is designed to alleviate the negative effect of target samples with noisy pseudo labels and raise the capacity of visual prompts to spatial perturbations. Extensive experiments demonstrate that Uni-UVPT achieves state-of-the-art performance on GTA5 $\to$ Cityscapes and SYNTHIA $\to$ Cityscapes tasks and can serve as a universal and parameter-efficient framework for large-model unsupervised knowledge transfer. Code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/uni-uvpt and https://github.com/huawei-noah/noah-research/tree/master/uni-uvpt. | null |
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method | https://papers.nips.cc/paper_files/paper/2023/hash/15ce36d35622f126f38e90167de1a350-Abstract-Conference.html | Ahmed Khaled, Konstantin Mishchenko, Chi Jin | https://papers.nips.cc/paper_files/paper/2023/hash/15ce36d35622f126f38e90167de1a350-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20047-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15ce36d35622f126f38e90167de1a350-Paper-Conference.pdf | null | This paper proposes a new easy-to-implement parameter-free gradient-based optimizer: DoWG (Distance over Weighted Gradients). We prove that DoWG is efficient---matching the convergence rate of optimally tuned gradient descent in convex optimization up to a logarithmic factor without tuning any parameters, and universal---automatically adapting to both smooth and nonsmooth problems. While popular algorithms following the AdaGrad framework compute a running average of the squared gradients, DoWG maintains a new distance-based weighted version of the running average, which is crucial to achieve the desired properties. To complement our theory, we also show empirically that DoWG trains at the edge of stability, and validate its effectiveness on practical machine learning tasks. | null |
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning | https://papers.nips.cc/paper_files/paper/2023/hash/15d15045f93b44d933a260b249608d43-Abstract-Conference.html | Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi, Andreas Krause | https://papers.nips.cc/paper_files/paper/2023/hash/15d15045f93b44d933a260b249608d43-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20358-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15d15045f93b44d933a260b249608d43-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15d15045f93b44d933a260b249608d43-Supplemental-Conference.pdf | Multitask learning is a powerful framework that enables one to simultaneously learn multiple related tasks by sharing information between them. Quantifying uncertainty in the estimated tasks is of pivotal importance for many downstream applications, such as online or active learning. In this work, we provide novel confidence intervals for multitask regression in the challenging agnostic setting, i.e., when neither the similarity between tasks nor the tasks' features are available to the learner. The obtained intervals do not require i.i.d. data and can be directly applied to bound the regret in online learning. Through a refined analysis of the multitask information gain, we obtain new regret guarantees that, depending on a task similarity parameter, can significantly improve over treating tasks independently. We further propose a novel online learning algorithm that achieves such improved regret without knowing this parameter in advance, i.e., automatically adapting to task similarity. As a second key application of our results, we introduce a novel multitask active learning setup where several tasks must be simultaneously optimized, but only one of them can be queried for feedback by the learner at each round. For this problem, we design a no-regret algorithm that uses our confidence intervals to decide which task should be queried. Finally, we empirically validate our bounds and algorithms on synthetic and real-world (drug discovery) data. | null |
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation | https://papers.nips.cc/paper_files/paper/2023/hash/15d3d4a4bd808605e3a3c1ea0fd0eba4-Abstract-Conference.html | Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma | https://papers.nips.cc/paper_files/paper/2023/hash/15d3d4a4bd808605e3a3c1ea0fd0eba4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19609-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15d3d4a4bd808605e3a3c1ea0fd0eba4-Paper-Conference.pdf | null | Recent studies in reinforcement learning (RL) have made significant progress by leveraging function approximation to alleviate the sample complexity hurdle for better performance. Despite the success, existing provably efficient algorithms typically rely on the accessibility of immediate feedback upon taking actions. The failure to account for the impact of delay in observations can significantly degrade the performance of real-world systems due to the regret blow-up. In this work, we tackle the challenge of delayed feedback in RL with linear function approximation by employing posterior sampling, which has been shown to empirically outperform the popular UCB algorithms in a wide range of regimes. We first introduce \textit{Delayed-PSVI}, an optimistic value-based algorithm that effectively explores the value function space via noise perturbation with posterior sampling. We provide the first analysis for posterior sampling algorithms with delayed feedback in RL and show our algorithm achieves $\widetilde{O}(\sqrt{d^3H^3 T} + d^2H^2 \mathbb{E}[\tau])$ worst-case regret in the presence of unknown stochastic delays. Here $\mathbb{E}[\tau]$ is the expected delay. To further improve its computational efficiency and to expand its applicability in high-dimensional RL problems, we incorporate a gradient-based approximate sampling scheme via Langevin dynamics for \textit{Delayed-LPSVI}, which maintains the same order-optimal regret guarantee with $\widetilde{O}(dHK)$ computational cost. Empirical evaluations are performed to demonstrate the statistical and computational efficacy of our algorithms. | null |
Macro Placement by Wire-Mask-Guided Black-Box Optimization | https://papers.nips.cc/paper_files/paper/2023/hash/15d6717f8bb33b3a74df26ce1eee0b9a-Abstract-Conference.html | Yunqi Shi, Ke Xue, Song Lei, Chao Qian | https://papers.nips.cc/paper_files/paper/2023/hash/15d6717f8bb33b3a74df26ce1eee0b9a-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21885-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15d6717f8bb33b3a74df26ce1eee0b9a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15d6717f8bb33b3a74df26ce1eee0b9a-Supplemental-Conference.zip | The development of very large-scale integration (VLSI) technology has posed new challenges for electronic design automation (EDA) techniques in chip floorplanning. During this process, macro placement is an important subproblem, which tries to determine the positions of all macros with the aim of minimizing half-perimeter wirelength (HPWL) and avoiding overlapping. Previous methods include packing-based, analytical and reinforcement learning methods. In this paper, we propose a new black-box optimization (BBO) framework (called WireMask-BBO) for macro placement, by using a wire-mask-guided greedy procedure for objective evaluation. Equipped with different BBO algorithms, WireMask-BBO empirically achieves significant improvements over previous methods, i.e., achieves significantly shorter HPWL by using much less time. Furthermore, it can fine-tune existing placements by treating them as initial solutions, which can bring up to 50% improvement in HPWL. WireMask-BBO has the potential to significantly improve the quality and efficiency of chip floorplanning, which makes it appealing to researchers and practitioners in EDA and will also promote the application of BBO. Our code is available at https://github.com/lamda-bbo/WireMask-BBO. | null |
Reconciling Competing Sampling Strategies of Network Embedding | https://papers.nips.cc/paper_files/paper/2023/hash/15dc2344ea9bdc01ffb8bb2d692e4018-Abstract-Conference.html | Yuchen Yan, Baoyu Jing, Lihui Liu, Ruijie Wang, Jinning Li, Tarek Abdelzaher, Hanghang Tong | https://papers.nips.cc/paper_files/paper/2023/hash/15dc2344ea9bdc01ffb8bb2d692e4018-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21308-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15dc2344ea9bdc01ffb8bb2d692e4018-Paper-Conference.pdf | null | Network embedding plays a significant role in a variety of applications. To capture the topology of the network, most of the existing network embedding algorithms follow a sampling training procedure, which maximizes the similarity (e.g., embedding vectors' dot product) between positively sampled node pairs and minimizes the similarity between negatively sampled node pairs in the embedding space. Typically, close node pairs function as positive samples while distant node pairs are usually considered as negative samples. However, under different or even competing sampling strategies, some methods champion sampling distant node pairs as positive samples to encapsulate longer distance information in link prediction, whereas others advocate adding close nodes into the negative sample set to boost the performance of node recommendation. In this paper, we seek to understand the intrinsic relationships between these competing strategies. To this end, we identify two properties (discrimination and monotonicity) that given any node pair proximity distribution, node embeddings should embrace.Moreover, we quantify the empirical error of the trained similarity score w.r.t. the sampling strategy, which leads to an important finding that the discrimination property and the monotonicity property for all node pairs can not be satisfied simultaneously in real-world applications. Guided by such analysis, a simple yet novel model (SENSEI) is proposed, which seamlessly fulfills the discrimination property and the partial monotonicity within the top-$K$ ranking list. Extensive experiments show that SENSEI outperforms the state-of-the-arts in plain network embedding. | null |
Zero-shot causal learning | https://papers.nips.cc/paper_files/paper/2023/hash/15ddb1773510075ef44981cdb204330b-Abstract-Conference.html | Hamed Nilforoshan, Michael Moor, Yusuf Roohani, Yining Chen, Anja Šurina, Michihiro Yasunaga, Sara Oblak, Jure Leskovec | https://papers.nips.cc/paper_files/paper/2023/hash/15ddb1773510075ef44981cdb204330b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20382-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15ddb1773510075ef44981cdb204330b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15ddb1773510075ef44981cdb204330b-Supplemental-Conference.zip | Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. However, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address.Here, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug's attributes) and individual features (e.g., a patient's history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, CaML's zero-shot predictions outperform even strong baselines trained directly on data from the test interventions. | null |
Learning Modulated Transformation in GANs | https://papers.nips.cc/paper_files/paper/2023/hash/15f1dbc086bfd94d8c32557b573cbe18-Abstract-Conference.html | Ceyuan Yang, Qihang Zhang, Yinghao Xu, Jiapeng Zhu, Yujun Shen, Bo Dai | https://papers.nips.cc/paper_files/paper/2023/hash/15f1dbc086bfd94d8c32557b573cbe18-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20135-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15f1dbc086bfd94d8c32557b573cbe18-Paper-Conference.pdf | null | The success of style-based generators largely benefits from style modulation,which helps take care of the cross-instance variation within data. However, theinstance-wise stochasticity is typically introduced via regular convolution, wherekernels interact with features at some fixed locations, limiting its capacity formodeling geometric variation. To alleviate this problem, we equip the generatorin generative adversarial networks (GANs) with a plug-and-play module, termedas modulated transformation module (MTM). This module predicts spatial offsetsunder the control of latent codes, based on which the convolution operation canbe applied at variable locations for different instances, and hence offers the modelan additional degree of freedom to handle geometry deformation. Extensiveexperiments suggest that our approach can be faithfully generalized to variousgenerative tasks, including image generation, 3D-aware image synthesis, andvideo generation, and get compatible with state-of-the-art frameworks withoutany hyper-parameter tuning. It is noteworthy that, towards human generation onthe challenging TaiChi dataset, we improve the FID of StyleGAN3 from 21.36 to13.60, demonstrating the efficacy of learning modulated geometry transformation.Code and models are available at https://github.com/limbo0000/mtm. | null |
Active Negative Loss Functions for Learning with Noisy Labels | https://papers.nips.cc/paper_files/paper/2023/hash/15f4cefb0e143c7ad9d40e879b0a9d0c-Abstract-Conference.html | Xichen Ye, Xiaoqiang Li, songmin dai, Tong Liu, Yan Sun, Weiqin Tong | https://papers.nips.cc/paper_files/paper/2023/hash/15f4cefb0e143c7ad9d40e879b0a9d0c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20717-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15f4cefb0e143c7ad9d40e879b0a9d0c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15f4cefb0e143c7ad9d40e879b0a9d0c-Supplemental-Conference.zip | Robust loss functions are essential for training deep neural networks in the presence of noisy labels. Some robust loss functions use Mean Absolute Error (MAE) as its necessary component. For example, the recently proposed Active Passive Loss (APL) uses MAE as its passive loss function. However, MAE treats every sample equally, slows down the convergence and can make training difficult. In this work, we propose a new class of theoretically robust passive loss functions different from MAE, namely Normalized Negative Loss Functions (NNLFs), which focus more on memorized clean samples. By replacing the MAE in APL with our proposed NNLFs, we improve APL and propose a new framework called Active Negative Loss (ANL). Experimental results on benchmark and real-world datasets demonstrate that the new set of loss functions created by our ANL framework can outperform state-of-the-art methods. The code is available athttps://github.com/Virusdoll/Active-Negative-Loss. | null |
Compositional Generalization from First Principles | https://papers.nips.cc/paper_files/paper/2023/hash/15f6a10899f557ce53fe39939af6f930-Abstract-Conference.html | Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel | https://papers.nips.cc/paper_files/paper/2023/hash/15f6a10899f557ce53fe39939af6f930-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20245-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/15f6a10899f557ce53fe39939af6f930-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/15f6a10899f557ce53fe39939af6f930-Supplemental-Conference.zip | Leveraging the compositional nature of our world to expedite learning and facilitate generalization is a hallmark of human perception. In machine learning, on the other hand, achieving compositional generalization has proven to be an elusive goal, even for models with explicit compositional priors. To get a better handle on compositional generalization, we here approach it from the bottom up: Inspired by identifiable representation learning, we investigate compositionality as a property of the data-generating process rather than the data itself. This reformulation enables us to derive mild conditions on only the support of the training distribution and the model architecture, which are sufficient for compositional generalization. We further demonstrate how our theoretical framework applies to real-world scenarios and validate our findings empirically. Our results set the stage for a principled theoretical study of compositional generalization. | null |
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas | https://papers.nips.cc/paper_files/paper/2023/hash/16049e0c3f47899091ac46f8b3afb178-Abstract-Conference.html | Zheng Chen, Yan-Pei Cao, Yuan-Chen Guo, Chen Wang, Ying Shan, Song-Hai Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/16049e0c3f47899091ac46f8b3afb178-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20880-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16049e0c3f47899091ac46f8b3afb178-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16049e0c3f47899091ac46f8b3afb178-Supplemental-Conference.zip | Achieving an immersive experience enabling users to explore virtual environments with six degrees of freedom (6DoF) is essential for various applications such as virtual reality (VR). Wide-baseline panoramas are commonly used in these applications to reduce network bandwidth and storage requirements. However, synthesizing novel views from these panoramas remains a key challenge. Although existing neural radiance field methods can produce photorealistic views under narrow-baseline and dense image captures, they tend to overfit the training views when dealing with wide-baseline panoramas due to the difficulty in learning accurate geometry from sparse $360^{\circ}$ views. To address this problem, we propose PanoGRF, Generalizable Spherical Radiance Fields for Wide-baseline Panoramas, which construct spherical radiance fields incorporating $360^{\circ}$ scene priors. Unlike generalizable radiance fields trained on perspective images, PanoGRF avoids the information loss from panorama-to-perspective conversion and directly aggregates geometry and appearance features of 3D sample points from each panoramic view based on spherical projection. Moreover, as some regions of the panorama are only visible from one view while invisible from others under wide baseline settings, PanoGRF incorporates $360^{\circ}$ monocular depth priors into spherical depth estimation to improve the geometry features. Experimental results on multiple panoramic datasets demonstrate that PanoGRF significantly outperforms state-of-the-art generalizable view synthesis methods for wide-baseline panoramas (e.g., OmniSyn) and perspective images (e.g., IBRNet, NeuRay). | null |
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction | https://papers.nips.cc/paper_files/paper/2023/hash/16063a1c0f0cddd4894585cf44cebb2c-Abstract-Conference.html | Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian Adelstein, Smita Krishnaswamy | https://papers.nips.cc/paper_files/paper/2023/hash/16063a1c0f0cddd4894585cf44cebb2c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21341-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16063a1c0f0cddd4894585cf44cebb2c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16063a1c0f0cddd4894585cf44cebb2c-Supplemental-Conference.pdf | Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE). | null |
Finite-Time Analysis of Single-Timescale Actor-Critic | https://papers.nips.cc/paper_files/paper/2023/hash/160adf2dc118a920e7858484b92a37d8-Abstract-Conference.html | Xuyang Chen, Lin Zhao | https://papers.nips.cc/paper_files/paper/2023/hash/160adf2dc118a920e7858484b92a37d8-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20660-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/160adf2dc118a920e7858484b92a37d8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/160adf2dc118a920e7858484b92a37d8-Supplemental-Conference.pdf | Actor-critic methods have achieved significant success in many challenging applications. However, its finite-time convergence is still poorly understood in the most practical single-timescale form. Existing works on analyzing single-timescale actor-critic have been limited to i.i.d. sampling or tabular setting for simplicity. We investigate the more practical online single-timescale actor-critic algorithm on continuous state space, where the critic assumes linear function approximation and updates with a single Markovian sample per actor step. Previous analysis has been unable to establish the convergence for such a challenging scenario. We demonstrate that the online single-timescale actor-critic method provably finds an $\epsilon$-approximate stationary point with $\widetilde{\mathcal{O}}(\epsilon^{-2})$ sample complexity under standard assumptions, which can be further improved to $\mathcal{O}(\epsilon^{-2})$ under the i.i.d. sampling. Our novel framework systematically evaluates and controls the error propagation between the actor and critic. It offers a promising approach for analyzing other single-timescale reinforcement learning algorithms as well. | null |
VanillaNet: the Power of Minimalism in Deep Learning | https://papers.nips.cc/paper_files/paper/2023/hash/16336d94a5ffca8de019087ab7fe403f-Abstract-Conference.html | Hanting Chen, Yunhe Wang, Jianyuan Guo, Dacheng Tao | https://papers.nips.cc/paper_files/paper/2023/hash/16336d94a5ffca8de019087ab7fe403f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22746-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16336d94a5ffca8de019087ab7fe403f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16336d94a5ffca8de019087ab7fe403f-Supplemental-Conference.pdf | At the heart of foundation models is the philosophy of "more is different", exemplified by the astonishing success in computer vision and natural language processing. However, the challenges of optimization and inherent complexity of transformer models call for a paradigm shift towards simplicity. In this study, we introduce VanillaNet, a neural network architecture that embraces elegance in design. By avoiding high depth, shortcuts, and intricate operations like self-attention, VanillaNet is refreshingly concise yet remarkably powerful. Each layer is carefully crafted to be compact and straightforward, with nonlinear activation functions pruned after training to restore the original architecture. VanillaNet overcomes the challenges of inherent complexity, making it ideal for resource-constrained environments. Its easy-to-understand and highly simplified architecture opens new possibilities for efficient deployment. Extensive experimentation demonstrates that VanillaNet delivers performance on par with renowned deep neural networks and vision transformers, showcasing the power of minimalism in deep learning. This visionary journey of VanillaNet has significant potential to redefine the landscape and challenge the status quo of foundation model, setting a new path for elegant and effective model design. Pre-trained models and codes are available at https://github.com/huawei-noah/VanillaNet and https://gitee.com/mindspore/models/tree/master/research/cv/vanillanet | null |
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs | https://papers.nips.cc/paper_files/paper/2023/hash/16347f6e665376fd9a9a290dbfe0db5b-Abstract-Conference.html | Dominik Straub, Matthias Schultheis, Heinz Koeppl, Constantin A. Rothkopf | https://papers.nips.cc/paper_files/paper/2023/hash/16347f6e665376fd9a9a290dbfe0db5b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19876-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16347f6e665376fd9a9a290dbfe0db5b-Paper-Conference.pdf | null | Inverse optimal control can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, is limited to fully observable or linear systems, or requires the action signals to be known. Here, we introduce a probabilistic approach to inverse optimal control for partially observable stochastic non-linear systems with unobserved action signals, which unifies previous approaches to inverse optimal control with maximum causal entropy formulations. Using an explicit model of the noise characteristics of the sensory and motor systems of the agent in conjunction with local linearization techniques, we derive an approximate likelihood function for the model parameters, which can be computed within a single forward pass. We present quantitative evaluations on stochastic and partially observable versions of two classic control tasks and two human behavioral tasks. Importantly, we show that our method can disentangle perceptual factors and behavioral costs despite the fact that epistemic and pragmatic actions are intertwined in sequential decision-making under uncertainty, such as in active sensing and active learning. The proposed method has broad applicability, ranging from imitation learning to sensorimotor neuroscience. | null |
TIES-Merging: Resolving Interference When Merging Models | https://papers.nips.cc/paper_files/paper/2023/hash/1644c9af28ab7916874f6fd6228a9bcf-Abstract-Conference.html | Prateek Yadav, Derek Tam, Leshem Choshen, Colin A. Raffel, Mohit Bansal | https://papers.nips.cc/paper_files/paper/2023/hash/1644c9af28ab7916874f6fd6228a9bcf-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19593-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1644c9af28ab7916874f6fd6228a9bcf-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1644c9af28ab7916874f6fd6228a9bcf-Supplemental-Conference.zip | Transfer learning – i.e., further fine-tuning a pre-trained model on a downstream task – can confer significant advantages, including improved downstream performance, faster convergence, and better sample efficiency. These advantages have led to a proliferation of task-specific fine-tuned models, which typically can only perform a single task and do not benefit from one another. Recently, model merging techniques have emerged as a solution to combine multiple task-specific models into a single multitask model without performing additional training. However, existing merging methods often ignore the interference between parameters of different models, resulting in large performance drops when merging multiple models. In this paper, we demonstrate that prior merging techniques inadvertently lose valuable information due to two major sources of interference: (a) interference due to redundant parameter values and (b) disagreement on the sign of a given parameter’s values across models. To address this, we propose our method, TrIm, Elect Sign & Merge (TIES-Merging), which introduces three novel steps when merging models: (1) resetting parameters that only changed a small amount during fine-tuning, (2) resolving sign conflicts, and (3) merging only the parameters that are in alignment with the final agreed-upon sign. We find that TIES-Merging outperforms existing methods in diverse settings covering a range of modalities, domains, number of tasks, model sizes, architectures, and fine-tuning settings. We further analyze the impact of different types of interference on model parameters, highlight the importance of signs, and show that estimating the signs using the validation data could further improve performance. | null |
3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes | https://papers.nips.cc/paper_files/paper/2023/hash/164687cb815daae754d33364716e65e6-Abstract-Conference.html | Haotian Xue, Antonio Torralba, Josh Tenenbaum, Dan Yamins, Yunzhu Li, Hsiao-Yu Tung | https://papers.nips.cc/paper_files/paper/2023/hash/164687cb815daae754d33364716e65e6-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20886-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/164687cb815daae754d33364716e65e6-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/164687cb815daae754d33364716e65e6-Supplemental-Conference.pdf | Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. Our method is composed of a conditional Neural Radiance Field (NeRF)-style visual frontend and a 3D point-based dynamics prediction backend, using which we can impose strong relational and structural inductive bias to capture the structure of the underlying environment. Unlike existing intuitive point-based dynamics works that rely on the supervision of dense point trajectory from simulators, we relax the requirements and only assume access to multi-view RGB images and (imperfect) instance masks acquired using color prior. This enables the proposed model to handle scenarios where accurate point estimation and tracking are hard or impossible. We generate datasets including three challenging scenarios involving fluid, granular materials, and rigid objects in the simulation. The datasets do not include any dense particle information so most previous 3D-based intuitive physics pipelines can barely deal with that. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space. We also show that once trained, our model can achieve strong generalization in complex scenarios under extrapolate settings. | null |
Entropy-based Training Methods for Scalable Neural Implicit Samplers | https://papers.nips.cc/paper_files/paper/2023/hash/1646e34971facbcda3727d1dc28ab635-Abstract-Conference.html | Weijian Luo, Boya Zhang, Zhihua Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/1646e34971facbcda3727d1dc28ab635-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20407-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1646e34971facbcda3727d1dc28ab635-Paper-Conference.pdf | null | Efficiently sampling from un-normalized target distributions is a fundamental problem in scientific computing and machine learning. Traditional approaches such as Markov Chain Monte Carlo (MCMC) guarantee asymptotically unbiased samples from such distributions but suffer from computational inefficiency, particularly when dealing with high-dimensional targets, as they require numerous iterations to generate a batch of samples. In this paper, we introduce an efficient and scalable neural implicit sampler that overcomes these limitations. The implicit sampler can generate large batches of samples with low computational costs by leveraging a neural transformation that directly maps easily sampled latent vectors to target samples without the need for iterative procedures. To train the neural implicit samplers, we introduce two novel methods: the KL training method and the Fisher training method. The former method minimizes the Kullback-Leibler divergence, while the latter minimizes the Fisher divergence between the sampler and the target distributions. By employing the two training methods, we effectively optimize the neural implicit samplers to learn and generate from the desired target distribution. To demonstrate the effectiveness, efficiency, and scalability of our proposed samplers, we evaluate them on three sampling benchmarks with different scales. These benchmarks include sampling from 2D targets, Bayesian inference, and sampling from high-dimensional energy-based models (EBMs). Notably, in the experiment involving high-dimensional EBMs, our sampler produces samples that are comparable to those generated by MCMC-based methods while being more than 100 times more efficient, showcasing the efficiency of our neural sampler. Besides the theoretical contributions and strong empirical performances, the proposed neural samplers and corresponding training methods will shed light on further research on developing efficient samplers for various applications beyond the ones explored in this study. | null |
Direct Diffusion Bridge using Data Consistency for Inverse Problems | https://papers.nips.cc/paper_files/paper/2023/hash/165b0e600b1721bd59526131eb061092-Abstract-Conference.html | Hyungjin Chung, Jeongsol Kim, Jong Chul Ye | https://papers.nips.cc/paper_files/paper/2023/hash/165b0e600b1721bd59526131eb061092-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22035-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/165b0e600b1721bd59526131eb061092-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/165b0e600b1721bd59526131eb061092-Supplemental-Conference.pdf | Diffusion model-based inverse problem solvers have shown impressive performance, but are limited in speed, mostly as they require reverse diffusion sampling starting from noise. Several recent works have tried to alleviate this problem by building a diffusion process, directly bridging the clean and the corrupted for specific inverse problems. In this paper, we first unify these existing works under the name Direct Diffusion Bridges (DDB), showing that while motivated by different theories, the resulting algorithms only differ in the choice of parameters. Then, we highlight a critical limitation of the current DDB framework, namely that it does not ensure data consistency. To address this problem, we propose a modified inference procedure that imposes data consistency without the need for fine-tuning. We term the resulting method data Consistent DDB (CDDB), which outperforms its inconsistent counterpart in terms of both perception and distortion metrics, thereby effectively pushing the Pareto-frontier toward the optimum. Our proposed method achieves state-of-the-art results on both evaluation criteria, showcasing its superiority over existing methods. Code is open-sourced here. | null |
Mask Propagation for Efficient Video Semantic Segmentation | https://papers.nips.cc/paper_files/paper/2023/hash/167bcf2af2cd08fcf75b932022db0311-Abstract-Conference.html | Yuetian Weng, Mingfei Han, Haoyu He, Mingjie Li, Lina Yao, Xiaojun Chang, Bohan Zhuang | https://papers.nips.cc/paper_files/paper/2023/hash/167bcf2af2cd08fcf75b932022db0311-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20958-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/167bcf2af2cd08fcf75b932022db0311-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/167bcf2af2cd08fcf75b932022db0311-Supplemental-Conference.pdf | Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending image semantic segmentation models to exploit temporal relationships across video frames; however, these approaches often incur significant computational costs. In this paper, we propose an efficient mask propagation framework for VSS, called MPVSS. Our approach first employs a strong query-based image segmentor on sparse key frames to generate accurate binary masks and class predictions. We then design a flow estimation module utilizing the learned queries to generate a set of segment-aware flow maps, each associated with a mask prediction from the key frame. Finally, the mask-flow pairs are warped to serve as the mask predictions for the non-key frames. By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs. Extensive experiments on VSPW and Cityscapes demonstrate that our mask propagation framework achieves SOTA accuracy and efficiency trade-offs. For instance, our best model with Swin-L backbone outperforms the SOTA MRCFA using MiT-B5 by 4.0% mIoU, requiring only 26% FLOPs on the VSPW dataset. Moreover, our framework reduces up to 4× FLOPs compared to the per-frame Mask2Former baseline with only up to 2% mIoU degradation on the Cityscapes validation set. Code is available at https://github.com/ziplab/MPVSS. | null |
Private Distribution Learning with Public Data: The View from Sample Compression | https://papers.nips.cc/paper_files/paper/2023/hash/1687466683649e8bdcdec0e3f5c8de64-Abstract-Conference.html | Shai Ben-David, Alex Bie, Clément L Canonne, Gautam Kamath, Vikrant Singhal | https://papers.nips.cc/paper_files/paper/2023/hash/1687466683649e8bdcdec0e3f5c8de64-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20472-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1687466683649e8bdcdec0e3f5c8de64-Paper-Conference.pdf | null | We study the problem of private distribution learning with access to public data. In this setup, which we refer to as *public-private learning*, the learner is given public and private samples drawn from an unknown distribution $p$ belonging to a class $\mathcal Q$, with the goal of outputting an estimate of $p$ while adhering to privacy constraints (here, pure differential privacy) only with respect to the private samples. We show that the public-private learnability of a class $\mathcal Q$ is connected to the existence of a sample compression scheme for $\mathcal Q$, as well as to an intermediate notion we refer to as \emph{list learning}. Leveraging this connection: (1) approximately recovers previous results on Gaussians over $\mathbb R^d$; and (2) leads to new ones, including sample complexity upper bounds for arbitrary $k$-mixtures of Gaussians over $\mathbb R^d$, results for agnostic and distribution-shift resistant learners, as well as closure properties for public-private learnability under taking mixtures and products of distributions. Finally, via the connection to list learning, we show that for Gaussians in $\mathbb R^d$, at least $d$ public samples are necessary for private learnability, which is close to the known upper bound of $d+1$ public samples. | null |
Fitting trees to $\ell_1$-hyperbolic distances | https://papers.nips.cc/paper_files/paper/2023/hash/16bce4070c4e23434451b180348e3814-Abstract-Conference.html | Joon-Hyeok Yim, Anna Gilbert | https://papers.nips.cc/paper_files/paper/2023/hash/16bce4070c4e23434451b180348e3814-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22876-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16bce4070c4e23434451b180348e3814-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16bce4070c4e23434451b180348e3814-Supplemental-Conference.zip | Building trees to represent or to fit distances is a critical component of phylogenetic analysis, metric embeddings, approximation algorithms, geometric graph neural nets, and the analysis of hierarchical data. Much of the previous algorithmic work, however, has focused on generic metric spaces (i.e., those with no \emph{a priori} constraints). Leveraging several ideas from the mathematical analysis of hyperbolic geometry and geometric group theory, we study the tree fitting problem as finding the relation between the hyperbolicity (ultrametricity) vector and the error of tree (ultrametric) embedding. That is, we define a vector of hyperbolicity (ultrametric) values over all triples of points and compare the $\ell_p$ norms of this vector with the $\ell_q$ norm of the distortion of the best tree fit to the distances. This formulation allows us to define the average hyperbolicity (ultrametricity) in terms of a normalized $\ell_1$ norm of the hyperbolicity vector. Furthermore, we can interpret the classical tree fitting result of Gromov as a $p = q = \infty$ result. We present an algorithm \textsc{HCCRootedTreeFit} such that the $\ell_1$ error of the output embedding is analytically bounded in terms of the $\ell_1$-norm of the hyperbolicity vector (i.e., $p = q = 1$) and that this result is tight. Furthermore, this algorithm has significantly different theoretical and empirical performance as compared to Gromov's result and related algorithms. Finally, we show using \textsc{HCCRootedTreeFit} and related tree fitting algorithms, that supposedly standard data sets for hierarchical data analysis and geometric graph neural networks have radically different tree fits than those of synthetic, truly tree-like data sets, suggesting that a much more refined analysis of these standard data sets is called for. | null |
Learning Robust Statistics for Simulation-based Inference under Model Misspecification | https://papers.nips.cc/paper_files/paper/2023/hash/16c5b4102a6b6eb061e502ce6736ad8a-Abstract-Conference.html | Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi, Samuel Kaski | https://papers.nips.cc/paper_files/paper/2023/hash/16c5b4102a6b6eb061e502ce6736ad8a-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19569-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16c5b4102a6b6eb061e502ce6736ad8a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16c5b4102a6b6eb061e502ce6736ad8a-Supplemental-Conference.zip | Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models. However, such methods are known to yield untrustworthy and misleading inference outcomes under model misspecification, thus hindering their widespread applicability. In this work, we propose the first general approach to handle model misspecification that works across different classes of SBI methods. Leveraging the fact that the choice of statistics determines the degree of misspecification in SBI, we introduce a regularized loss function that penalizes those statistics that increase the mismatch between the data and the model. Taking NPE and ABC as use cases, we demonstrate the superior performance of our method on high-dimensional time-series models that are artificially misspecified. We also apply our method to real data from the field of radio propagation where the model is known to be misspecified. We show empirically that the method yields robust inference in misspecified scenarios, whilst still being accurate when the model is well-specified. | null |
Block-State Transformers | https://papers.nips.cc/paper_files/paper/2023/hash/16ccd203e9e3696a7ab0dcf568316379-Abstract-Conference.html | Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin | https://papers.nips.cc/paper_files/paper/2023/hash/16ccd203e9e3696a7ab0dcf568316379-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21882-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16ccd203e9e3696a7ab0dcf568316379-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16ccd203e9e3696a7ab0dcf568316379-Supplemental-Conference.pdf | State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and efficiently scale to long sequences owing to their subquadratic runtime complexity.Originally designed for continuous signals, SSMs have shown superior performance on a plethora of tasks, in vision and audio; however, SSMs still lag Transformer performance in Language Modeling tasks.In this work, we propose a hybrid layer named Block-State Transformer (BST), that internally combines an SSM sublayer for long-range contextualization, and a Block Transformer sublayer for short-term representation of sequences.We study three different, and completely parallelizable, variants that integrate SSMs and block-wise attention.We show that our model outperforms similar Transformer-based architectures on language modeling perplexity and generalizes to longer sequences. In addition, the Block-State Transformer demonstrates a more than tenfold increase in speed at the layer level compared to the Block-Recurrent Transformer when model parallelization is employed. | null |
Explaining Predictive Uncertainty with Information Theoretic Shapley Values | https://papers.nips.cc/paper_files/paper/2023/hash/16e4be78e61a3897665fa01504e9f452-Abstract-Conference.html | David Watson, Joshua O'Hara, Niek Tax, Richard Mudd, Ido Guy | https://papers.nips.cc/paper_files/paper/2023/hash/16e4be78e61a3897665fa01504e9f452-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21627-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/16e4be78e61a3897665fa01504e9f452-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/16e4be78e61a3897665fa01504e9f452-Supplemental-Conference.zip | Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast, explaining the $\textit{uncertainty}$ of model outputs has received relatively little attention. We adapt the popular Shapley value framework to explain various types of predictive uncertainty, quantifying each feature's contribution to the conditional entropy of individual model outputs. We consider games with modified characteristic functions and find deep connections between the resulting Shapley values and fundamental quantities from information theory and conditional independence testing. We outline inference procedures for finite sample error rate control with provable guarantees, and implement efficient algorithms that perform well in a range of experiments on real and simulated data. Our method has applications to covariate shift detection, active learning, feature selection, and active feature-value acquisition. | null |
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning | https://papers.nips.cc/paper_files/paper/2023/hash/1700ad4e6252e8f2955909f96367b34d-Abstract-Conference.html | Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, Joao Monteiro | https://papers.nips.cc/paper_files/paper/2023/hash/1700ad4e6252e8f2955909f96367b34d-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22843-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1700ad4e6252e8f2955909f96367b34d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1700ad4e6252e8f2955909f96367b34d-Supplemental-Conference.pdf | Handling out-of-distribution (OOD) samples has become a major stake in the real-world deployment of machine learning systems. This work explores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples: unseen classes and adversarial perturbations. First, we pair self-supervised contrastive learning with the maximum mean discrepancy (MMD) two-sample test. This approach enables us to robustly test whether two independent sets of samples originate from the same distribution, and we demonstrate its effectiveness by discriminating between CIFAR-10 and CIFAR-10.1 with higher confidence than previous work. Motivated by this success, we introduce CADet (Contrastive Anomaly Detection), a novel method for OOD detection of single samples. CADet draws inspiration from MMD, but leverages the similarity between contrastive transformations of a same sample. CADet outperforms existing adversarial detection methods in identifying adversarially perturbed samples on ImageNet and achieves comparable performance to unseen label detection methods on two challenging benchmarks: ImageNet-O and iNaturalist. Significantly, CADet is fully self-supervised and requires neither labels for in-distribution samples nor access to OOD examples. | null |
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning | https://papers.nips.cc/paper_files/paper/2023/hash/1704fe7aaff33a54802b83a016050ab8-Abstract-Conference.html | Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter | https://papers.nips.cc/paper_files/paper/2023/hash/1704fe7aaff33a54802b83a016050ab8-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22383-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1704fe7aaff33a54802b83a016050ab8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/1704fe7aaff33a54802b83a016050ab8-Supplemental-Conference.pdf | Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL.Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations.To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency across a range of DL benchmarks and show its gains under informative expert input and robustness against poor expert beliefs. | null |
Towards Efficient Image Compression Without Autoregressive Models | https://papers.nips.cc/paper_files/paper/2023/hash/170dc3e41f2d03e327e04dbab0fccbfb-Abstract-Conference.html | Muhammad Salman Ali, Yeongwoong Kim, Maryam Qamar, Sung-Chang Lim, Donghyun Kim, Chaoning Zhang, Sung-Ho Bae, Hui Yong Kim | https://papers.nips.cc/paper_files/paper/2023/hash/170dc3e41f2d03e327e04dbab0fccbfb-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21546-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/170dc3e41f2d03e327e04dbab0fccbfb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/170dc3e41f2d03e327e04dbab0fccbfb-Supplemental-Conference.pdf | Recently, learned image compression (LIC) has garnered increasing interest with its rapidly improving performance surpassing conventional codecs. A key ingredient of LIC is a hyperprior-based entropy model, where the underlying joint probability of the latent image features is modeled as a product of Gaussian distributions from each latent element. Since latents from the actual images are not spatially independent, autoregressive (AR) context based entropy models were proposed to handle the discrepancy between the assumed distribution and the actual distribution. Though the AR-based models have proven effective, the computational complexity is significantly increased due to the inherent sequential nature of the algorithm. In this paper, we present a novel alternative to the AR-based approach that can provide a significantly better trade-off between performance and complexity. To minimize the discrepancy, we introduce a correlation loss that forces the latents to be spatially decorrelated and better fitted to the independent probability model. Our correlation loss is proved to act as a general plug-in for the hyperprior (HP) based learned image compression methods. The performance gain from our correlation loss is ‘free’ in terms of computation complexity for both inference time and decoding time. To our knowledge, our method gives the best trade-off between the complexity and performance: combined with the Checkerboard-CM, it attains 90% and when combined with ChARM-CM, it attains 98% of the AR-based BD-Rate gains yet is around 50 times and 30 times faster than AR-based methods respectively | null |
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents | https://papers.nips.cc/paper_files/paper/2023/hash/1737656c4dc65027939e47e4587ce95e-Abstract-Conference.html | Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/1737656c4dc65027939e47e4587ce95e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20116-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/1737656c4dc65027939e47e4587ce95e-Paper-Conference.pdf | null | De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/MolRL-MGPT. | null |
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior | https://papers.nips.cc/paper_files/paper/2023/hash/176a579942089c4cdc70136c567932ab-Abstract-Conference.html | Luke Travis, Kolyan Ray | https://papers.nips.cc/paper_files/paper/2023/hash/176a579942089c4cdc70136c567932ab-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21157-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/176a579942089c4cdc70136c567932ab-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/176a579942089c4cdc70136c567932ab-Supplemental-Conference.zip | We study pointwise estimation and uncertainty quantification for a sparse variational Gaussian process method with eigenvector inducing variables. For a rescaled Brownian motion prior, we derive theoretical guarantees and limitations for the frequentist size and coverage of pointwise credible sets. For sufficiently many inducing variables, we precisely characterize the asymptotic frequentist coverage, deducing when credible sets from this variational method are conservative and when overconfident/misleading. We numerically illustrate the applicability of our results and discuss connections with other common Gaussian process priors. | null |
Few-shot Generation via Recalling Brain-Inspired Episodic-Semantic Memory | https://papers.nips.cc/paper_files/paper/2023/hash/17826a22eb8b58494dfdfca61e772c39-Abstract-Conference.html | Zhibin Duan, Zhiyi Lv, Chaojie Wang, Bo Chen, Bo An, Mingyuan Zhou | https://papers.nips.cc/paper_files/paper/2023/hash/17826a22eb8b58494dfdfca61e772c39-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21991-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/17826a22eb8b58494dfdfca61e772c39-Paper-Conference.pdf | null | Aimed at adapting a generative model to a novel generation task with only a few given data samples, the capability of few-shot generation is crucial for many real-world applications with limited data, \emph{e.g.}, artistic domains.Instead of training from scratch, recent works tend to leverage the prior knowledge stored in previous datasets, which is quite similar to the memory mechanism of human intelligence, but few of these works directly imitate the memory-recall mechanism that humans make good use of in accomplishing creative tasks, \emph{e.g.}, painting and writing.Inspired by the memory mechanism of human brain, in this work, we carefully design a variational structured memory module (VSM), which can simultaneously store both episodic and semantic memories to assist existing generative models efficiently recall these memories during sample generation.Meanwhile, we introduce a bionic memory updating strategy for the conversion between episodic and semantic memories, which can also model the uncertainty during conversion.Then, we combine the developed VSM with various generative models under the Bayesian framework, and evaluate these memory-augmented generative models with few-shot generation tasks, demonstrating the effectiveness of our methods. | null |
Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces | https://papers.nips.cc/paper_files/paper/2023/hash/17a234c91f746d9625a75cf8a8731ee2-Abstract-Conference.html | Joseph Costello, Hisham Temmar, Luis Cubillos, Matthew Mender, Dylan Wallace, Matt Willsey, Parag Patil, Cynthia Chestek | https://papers.nips.cc/paper_files/paper/2023/hash/17a234c91f746d9625a75cf8a8731ee2-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20074-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/17a234c91f746d9625a75cf8a8731ee2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/17a234c91f746d9625a75cf8a8731ee2-Supplemental-Conference.zip | Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns. | null |
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