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TinyLUT: Tiny Look-Up Table for Efficient Image Restoration at the Edge | https://openreview.net/forum?id=tN0xnYPLt6 | https://openreview.net/forum?id=tN0xnYPLt6 | Huanan LI,Juntao Guan,Lai Rui,Sijun Ma,Lin Gu,Zhangming Zhu | NIPS 2024,Poster | Look-up tables(LUTs)-based methods have recently shown enormous potential in image restoration tasks, which are capable of significantly accelerating the inference. However, the size of LUT exhibits exponential growth with the convolution kernel size, creating a storage bottleneck for its broader application on edge devices. Here, we address the storage explosion challenge to promote the capacity of mapping the complex CNN models by LUT. We introduce an innovative separable mapping strategy to achieve over $7\times$ storage reduction, transforming the storage from exponential dependence on kernel size to a linear relationship. Moreover, we design a dynamic discretization mechanism to decompose the activation and compress the quantization scale that further shrinks the LUT storage by $4.48\times$. As a result, the storage requirement of our proposed TinyLUT is around 4.1\% of MuLUT-SDY-X2 and amenable to on-chip cache, yielding competitive accuracy with over $5\times$ lower inference latency on Raspberry 4B than FSRCNN. Our proposed TinyLUT enables superior inference speed on edge devices with new state-of-the-art accuracy on both of image super-resolution and denoising, showcasing the potential of applying this method to various image restoration tasks at the edge. The codes are available at: https://github.com/Jonas-KD/TinyLUT. | https://openreview.net/pdf/1df5ba9d709ef47fd00e399db6114e07034d4339.pdf |
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training | https://openreview.net/forum?id=GUccmOMBv6 | https://openreview.net/forum?id=GUccmOMBv6 | David Brandfonbrener,Hanlin Zhang,Andreas Kirsch,Jonathan Richard Schwarz,Sham M. Kakade | NIPS 2024,Poster | Selecting high-quality data for pre-training is crucial in shaping the downstream task performance of language models. A major challenge lies in identifying this optimal subset, a problem generally considered intractable, thus necessitating scalable and effective heuristics. In this work, we propose a data selection method, CoLoR-Filter (Conditional Loss Reduction Filtering), which leverages an empirical Bayes-inspired approach to derive a simple and computationally efficient selection criterion based on the relative loss values of two auxiliary models.
In addition to the modeling rationale, we evaluate CoLoR-Filter empirically on two language modeling tasks: (1) selecting data from C4 for domain adaptation to evaluation on Books and (2) selecting data from C4 for a suite of downstream multiple-choice question answering tasks. We demonstrate favorable scaling both as we subselect more aggressively and using small auxiliary models to select data for large target models. As one headline result, CoLoR-Filter data selected using a pair of 150m parameter auxiliary models can train a 1.2b parameter target model to match a 1.2b parameter model trained on 25b randomly selected tokens with 25x less data for Books and 11x less data for the downstream tasks.
Code: https://github.com/davidbrandfonbrener/color-filter-olmo
Filtered data: https://huggingface.co/datasets/davidbrandfonbrener/color-filtered-c4 | https://openreview.net/pdf/e639e5cb9a9b6a85d1607f14ab0742d340d48165.pdf |
Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure | https://openreview.net/forum?id=5cIRdGM1uG | https://openreview.net/forum?id=5cIRdGM1uG | Hanseul Cho,Jaeyoung Cha,Pranjal Awasthi,Srinadh Bhojanapalli,Anupam Gupta,Chulhee Yun | NIPS 2024,Poster | Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this problem, we propose *position coupling*, a simple yet effective method that directly embeds the structure of the tasks into the positional encoding of a (decoder-only) Transformer. Taking a departure from the vanilla absolute position mechanism assigning unique position IDs to each of the tokens, we assign the same position IDs to two or more "relevant" tokens; for integer addition tasks, we regard digits of the same significance as in the same position. On the empirical side, we show that with the proposed position coupling, our models trained on 1 to 30-digit additions can generalize up to *200-digit* additions (6.67x of the trained length). On the theoretical side, we prove that a 1-layer Transformer with coupled positions can solve the addition task involving exponentially many digits, whereas any 1-layer Transformer without positional information cannot entirely solve it. We also demonstrate that position coupling can be applied to other algorithmic tasks such as Nx2 multiplication and a two-dimensional task. Our codebase is available at [github.com/HanseulJo/position-coupling](https://github.com/HanseulJo/position-coupling). | https://openreview.net/pdf/103df0af7e400b66814f3dceaf95ed859b2d944f.pdf |
Invisible Image Watermarks Are Provably Removable Using Generative AI | https://openreview.net/forum?id=7hy5fy2OC6 | https://openreview.net/forum?id=7hy5fy2OC6 | Xuandong Zhao,Kexun Zhang,Zihao Su,Saastha Vasan,Ilya Grishchenko,Christopher Kruegel,Giovanni Vigna,Yu-Xiang Wang,Lei Li | NIPS 2024,Poster | Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models.
We propose a family of regeneration attacks to remove these invisible watermarks.
The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image.
This approach is flexible and can be instantiated with many existing image-denoising algorithms and pre-trained generative models such as diffusion models. Through formal proofs and extensive empirical evaluations, we demonstrate that pixel-level invisible watermarks are vulnerable to this regeneration attack.
Our results reveal that, across four different pixel-level watermarking schemes, the proposed method consistently achieves superior performance compared to existing attack techniques, with lower detection rates and higher image quality.
However, watermarks that keep the image semantically similar can be an alternative defense against our attacks.
Our finding underscores the need for a shift in research/industry emphasis from invisible watermarks to semantic-preserving watermarks. Code is available at https://github.com/XuandongZhao/WatermarkAttacker | https://openreview.net/pdf/ffea2d2c76fd07118cd2a1c52075d932d44f0ddf.pdf |
Stochastic Optimal Control for Diffusion Bridges in Function Spaces | https://openreview.net/forum?id=WyQW4G57Zd | https://openreview.net/forum?id=WyQW4G57Zd | Byoungwoo Park,Jungwon Choi,Sungbin Lim,Juho Lee | NIPS 2024,Poster | Recent advancements in diffusion models and diffusion bridges primarily focus on finite-dimensional spaces, yet many real-world problems necessitate operations in infinite-dimensional function spaces for more natural and interpretable formulations.
In this paper, we present a theory of stochastic optimal control (SOC) tailored to infinite-dimensional spaces, aiming to extend diffusion-based algorithms to function spaces.
Specifically, we demonstrate how Doob’s $h$-transform, the fundamental tool for constructing diffusion bridges, can be derived from the SOC perspective and expanded to infinite dimensions.
This expansion presents a challenge, as infinite-dimensional spaces typically lack closed-form densities.
Leveraging our theory, we establish that solving the optimal control problem with a specific objective function choice is equivalent to learning diffusion-based generative models.
We propose two applications: 1) learning bridges between two infinite-dimensional distributions and 2) generative models for sampling from an infinite-dimensional distribution.
Our approach proves effective for diverse problems involving continuous function space representations, such as resolution-free images, time-series data, and probability density functions. | https://openreview.net/pdf/6dc38b9c17ec695098e95def34f4ce97a1a745ed.pdf |
Efficient Discrepancy Testing for Learning with Distribution Shift | https://openreview.net/forum?id=ojIhvhQBAQ | https://openreview.net/forum?id=ojIhvhQBAQ | Gautam Chandrasekaran,Adam Klivans,Vasilis Kontonis,Konstantinos Stavropoulos,Arsen Vasilyan | NIPS 2024,Poster | A fundamental notion of distance between train and test distributions from the field of domain adaptation is discrepancy distance. While in general hard to compute, here we provide the first set of provably efficient algorithms for testing *localized* discrepancy distance, where discrepancy is computed with respect to a fixed output classifier. These results imply a broad set of new, efficient learning algorithms in the recently introduced model of Testable Learning with Distribution Shift (TDS learning) due to Klivans et al. (2023).
Our approach generalizes and improves all prior work on TDS learning: (1) we obtain *universal* learners that succeed simultaneously for large classes of test distributions, (2) achieve near-optimal error rates, and (3) give exponential improvements for constant depth circuits. Our methods further extend to semi-parametric settings and imply the first positive results for low-dimensional convex sets. Additionally, we separate learning and testing phases and obtain algorithms that run in fully polynomial time at test time. | https://openreview.net/pdf/8c9c8d6cd774059c113f60609819dd77cc2b2769.pdf |
A Unifying Normative Framework of Decision Confidence | https://openreview.net/forum?id=BRvGfN3Xfm | https://openreview.net/forum?id=BRvGfN3Xfm | Amelia Johnson,Michael A Buice,Koosha Khalvati | NIPS 2024,Poster | Self-assessment of one’s choices, i.e., confidence, is the topic of many decision neuroscience studies. Computational models of confidence, however, are limited to specific scenarios such as between choices with the same value. Here we present a normative framework for modeling decision confidence that is generalizable to various tasks and experimental setups. We further drive the implications of our model from both theoretical and experimental points of view. Specifically, we show that our model maps to the planning as an inference framework where the objective function is maximizing the gained reward and information entropy of the policy. Moreover, we validate our model on two different psychophysics experiments and show its superiority over other approaches in explaining subjects' confidence reports. | https://openreview.net/pdf/bad57d2f6699f41e9faffa00585fdc10015d1e23.pdf |
Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL | https://openreview.net/forum?id=dc4xbVfdzy | https://openreview.net/forum?id=dc4xbVfdzy | Qi Lv,Xiang Deng,Gongwei Chen,Michael Y Wang,Liqiang Nie | NIPS 2024,Poster | While the conditional sequence modeling with the transformer architecture has demonstrated its effectiveness in dealing with offline reinforcement learning (RL) tasks, it is struggle to handle out-of-distribution states and actions.
Existing work attempts to address this issue by data augmentation with the learned policy or adding extra constraints with the value-based RL algorithm. However, these studies still fail to overcome the following challenges: (1) insufficiently utilizing the historical temporal information among inter-steps, (2) overlooking the local intra-step relationships among states, actions and return-to-gos (RTGs), (3) overfitting suboptimal trajectories with noisy labels. To address these challenges, we propose $\textbf{D}$ecision $\textbf{M}$amba ($\textbf{DM}$), a novel multi-grained state space model (SSM) with a self-evolving policy learning strategy.
DM explicitly models the historical hidden state to extract the temporal information by using the mamba architecture. To capture the relationship among state-action-RTG triplets, a fine-grained SSM module is designed and integrated into the original coarse-grained SSM in mamba, resulting in a novel mamba architecture tailored for offline RL. Finally, to mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations. Extensive experiments on various tasks show that DM outperforms other baselines substantially. | https://openreview.net/pdf/bf1c3377f6b6a5448c29ed1730ada8e2d248ce23.pdf |
Model-Based Transfer Learning for Contextual Reinforcement Learning | https://openreview.net/forum?id=KLv1VLuMo8 | https://openreview.net/forum?id=KLv1VLuMo8 | Jung-Hoon Cho,Vindula Jayawardana,Sirui Li,Cathy Wu | NIPS 2024,Poster | Deep reinforcement learning (RL) is a powerful approach to complex decision-making. However, one issue that limits its practical application is its brittleness, sometimes failing to train in the presence of small changes in the environment. Motivated by the success of zero-shot transfer—where pre-trained models perform well on related tasks—we consider the problem of selecting a good set of training tasks to maximize generalization performance across a range of tasks. Given the high cost of training, it is critical to select training tasks strategically, but not well understood how to do so. We hence introduce Model-Based Transfer Learning (MBTL), which layers on top of existing RL methods to effectively solve contextual RL problems. MBTL models the generalization performance in two parts: 1) the performance set point, modeled using Gaussian processes, and 2) performance loss (generalization gap), modeled as a linear function of contextual similarity. MBTL combines these two pieces of information within a Bayesian optimization (BO) framework to strategically select training tasks. We show theoretically that the method exhibits sublinear regret in the number of training tasks and discuss conditions to further tighten regret bounds. We experimentally validate our methods using urban traffic and standard continuous control benchmarks. The experimental results suggest that MBTL can achieve up to 50x improved sample efficiency compared with canonical independent training and multi-task training. Further experiments demonstrate the efficacy of BO and the insensitivity to the underlying RL algorithm and hyperparameters. This work lays the foundations for investigating explicit modeling of generalization, thereby enabling principled yet effective methods for contextual RL. Code is available at https://github.com/jhoon-cho/MBTL/. | https://openreview.net/pdf/d2cc3180959ef0dcfdb2471da9ce763751f63ba2.pdf |
DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity | https://openreview.net/forum?id=IdQuUYMA1t | https://openreview.net/forum?id=IdQuUYMA1t | Baekrok Shin,Junsoo Oh,Hanseul Cho,Chulhee Yun | NIPS 2024,Poster | Warm-starting neural network training by initializing networks with previously learned weights is appealing, as practical neural networks are often deployed under a continuous influx of new data. However, it often leads to *loss of plasticity*, where the network loses its ability to learn new information, resulting in worse generalization than training from scratch. This occurs even under stationary data distributions, and its underlying mechanism is poorly understood. We develop a framework emulating real-world neural network training and identify noise memorization as the primary cause of plasticity loss when warm-starting on stationary data. Motivated by this, we propose **Direction-Aware SHrinking (DASH)**, a method aiming to mitigate plasticity loss by selectively forgetting memorized noise while preserving learned features. We validate our approach on vision tasks, demonstrating improvements in test accuracy and training efficiency. | https://openreview.net/pdf/1f7e991a14fd00e18381be7ccda7d2d45118e189.pdf |
Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization | https://openreview.net/forum?id=7qJFkuZdYo | https://openreview.net/forum?id=7qJFkuZdYo | Yuanpu Cao,Tianrong Zhang,Bochuan Cao,Ziyi Yin,Lu Lin,Fenglong Ma,Jinghui Chen | NIPS 2024,Poster | Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning seems to be a direct solution, it requires substantial computational resources and may significantly affect the utility of the original LLM.
Recent endeavors have introduced more lightweight strategies, focusing on extracting ``steering vectors'' to guide the model's output toward desired behaviors by adjusting activations within specific layers of the LLM's transformer architecture. However, such steering vectors are directly extracted from the activations of human preference data and thus often lead to suboptimal results and occasional failures, especially in alignment-related scenarios.
In this work, we propose an innovative approach that could produce more effective steering vectors through bi-directional preference optimization.
Our method is designed to allow steering vectors to directly influence the generation probability of contrastive human preference data pairs, thereby offering a more precise representation of the target behavior. By carefully adjusting the direction and magnitude of the steering vector, we enabled personalized control over the desired behavior across a spectrum of intensities.
Extensive experimentation across various open-ended generation tasks, particularly focusing on steering AI personas, has validated the efficacy of our approach.
Moreover, we comprehensively investigate critical alignment-concerning scenarios, such as managing truthfulness, mitigating hallucination, and addressing jailbreaking attacks alongside their respective defenses. Remarkably, our method can still demonstrate outstanding steering effectiveness across these scenarios. Furthermore, we showcase the transferability of our steering vectors across different models/LoRAs and highlight the synergistic benefits of applying multiple vectors simultaneously. These findings significantly broaden the practicality and versatility of our proposed method. | https://openreview.net/pdf/f3732528b64a68528a6adbc74189e17f4c6fa168.pdf |
A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning | https://openreview.net/forum?id=VQyb9LKmUH | https://openreview.net/forum?id=VQyb9LKmUH | Yuanning Cui,Zequn Sun,Wei Hu | NIPS 2024,Poster | Extensive knowledge graphs (KGs) have been constructed to facilitate knowledge-driven tasks across various scenarios. However, existing work usually develops separate reasoning models for different KGs, lacking the ability to generalize and transfer knowledge across diverse KGs and reasoning settings. In this paper, we propose a prompt-based KG foundation model via in-context learning, namely KG-ICL, to achieve a universal reasoning ability. Specifically, we introduce a prompt graph centered with a query-related example fact as context to understand the query relation. To encode prompt graphs with the generalization ability to unseen entities and relations in queries, we first propose a unified tokenizer that maps entities and relations in prompt graphs to predefined tokens. Then, we propose two message passing neural networks to perform prompt encoding and KG reasoning, respectively. We conduct evaluation on 43 different KGs in both transductive and inductive settings. Results indicate that the proposed KG-ICL outperforms baselines on most datasets, showcasing its outstanding generalization and universal reasoning capabilities. The source code is accessible on GitHub: https://github.com/nju-websoft/KG-ICL. | https://openreview.net/pdf/15c8fc9c778f0a77cd25ebff402ea4613ac94fef.pdf |
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making | https://openreview.net/forum?id=dG1HwKMYbC | https://openreview.net/forum?id=dG1HwKMYbC | Yangyang Yu,Zhiyuan Yao,Haohang Li,Zhiyang Deng,Yuechen Jiang,Yupeng Cao,Zhi Chen,Jordan W. Suchow,Zhenyu Cui,Rong Liu,Zhaozhuo Xu,Denghui Zhang,Koduvayur Subbalakshmi,GUOJUN XIONG,Yueru He,Jimin Huang,Dong Li,Qianqian Xie | NIPS 2024,Poster | Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a volatile environment for every decision, demanding sufficient intelligence to maximize returns and manage risks. Although LLMs have been used to develop agent systems that surpass human teams and yield impressive investment returns, opportunities to enhance multi-source information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce FinCon, an LLM-based multi-agent framework tailored for diverse financial tasks. Inspired by effective real-world investment firm organizational structures, FinCon utilizes a manager-analyst communication hierarchy. This structure allows for synchronized cross-functional agent collaboration towards unified goals through natural language interactions and equips each agent with greater memory capacity than humans. Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs. The conceptualized beliefs serve as verbal reinforcement for the future agent’s behavior and can be selectively propagated to the appropriate node that requires knowledge updates. This feature significantly improves performance while reducing unnecessary peer-to-peer communication costs. Moreover, FinCon demonstrates strong generalization capabilities in various financial tasks, including stock trading and portfolio management. | https://openreview.net/pdf/db337351a482134151dca292db9301d981d96463.pdf |
Global Rewards in Restless Multi-Armed Bandits | https://openreview.net/forum?id=3apt5AJ5QN | https://openreview.net/forum?id=3apt5AJ5QN | Naveen Janaki Raman,Zheyuan Ryan Shi,Fei Fang | NIPS 2024,Poster | Restless multi-armed bandits (RMAB) extend multi-armed bandits so arm pulls impact future arm states. Despite the success of RMABs, a key limiting assumption is the separability of rewards into a sum across arms. We address this deficiency by proposing restless-multi-armed bandit with global rewards (RMAB-G), a generalization of RMABs to global non-separable rewards. To solve RMAB-G, we develop the Linear-Whittle and Shapley-Whittle indices, which extend Whittle indices from RMABs to RMAB-Gs. We prove approximation bounds which demonstrate how Linear and Shapley-Whittle indices fail for non-linear rewards. To overcome this limitation, we propose two sets of adaptive policies: the first computes indices iteratively and the second combines indices with Monte-Carlo Tree Search (MCTS). Empirically, we demonstrate that adaptive policies outperform both pre-computed index policies and baselines in synthetic and real-world food rescue datasets. | https://openreview.net/pdf/c94e70e7b28d901a74a3fb2df5e9d23afe6565dd.pdf |
Large Language Model Unlearning via Embedding-Corrupted Prompts | https://openreview.net/forum?id=e5icsXBD8Q | https://openreview.net/forum?id=e5icsXBD8Q | Chris Yuhao Liu,Yaxuan Wang,Jeffrey Flanigan,Yang Liu | NIPS 2024,Poster | Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ensuring alignment and thus safe use. However, accurately and efficiently unlearning knowledge from an LLM remains challenging due to the potential collateral damage caused by the fuzzy boundary between retention and forgetting, and the large computational requirements for optimization across state-of-the-art models with hundreds of billions of parameters. In this work, we present \textbf{Embedding-COrrupted (ECO) Prompts}, a lightweight unlearning framework for large language models to address both the challenges of knowledge entanglement and unlearning efficiency. Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget. We learn corruptions added to prompt embeddings via zeroth order optimization toward the unlearning objective offline and corrupt prompts flagged by the classifier during inference. We find that these embedding-corrupted prompts not only lead to desirable outputs that satisfy the unlearning objective but also closely approximate the output from a model that has never been trained on the data intended for forgetting. Through extensive experiments on unlearning, we demonstrate the superiority of our method in achieving promising unlearning at \textit{nearly zero side effects} in general domains and domains closely related to the unlearned ones. Additionally, we highlight the scalability of our method to 100 LLMs, ranging from 0.5B to 236B parameters, incurring no additional cost as the number of parameters increases. We have made our code publicly available at \url{https://github.com/chrisliu298/llm-unlearn-eco}. | https://openreview.net/pdf/6ce60d7844fe2a3c3a9937127c591337d3945e16.pdf |
Euclidean distance compression via deep random features | https://openreview.net/forum?id=Fanbig8DR9 | https://openreview.net/forum?id=Fanbig8DR9 | Brett Leroux,Luis Rademacher | NIPS 2024,Poster | Motivated by the problem of compressing point sets into as few bits as possible while maintaining information about approximate distances between points, we construct random nonlinear maps $\varphi_\ell$ that compress point sets in the following way. For a point set $S$, the map $\varphi_\ell:\mathbb{R}^d \to N^{-1/2}\{-1,1\}^N$ has the property that storing $\varphi_\ell(S)$ (a sketch of $S$) allows one to report squared distances between points up to some multiplicative $(1\pm \epsilon)$ error with high probability. The maps $\varphi_\ell$ are the $\ell$-fold composition of a certain type of random feature mapping.
Compared to existing techniques, our maps offer several advantages. The standard method for compressing point sets by random mappings relies on the Johnson-Lindenstrauss lemma and involves compressing point sets with a random linear map. The main advantage of our maps $\varphi_\ell$ over random linear maps is that ours map point sets directly into the discrete cube $N^{-1/2}\{-1,1\}^N$ and so there is no additional step needed to convert the sketch to bits. For some range of parameters, our maps $\varphi_\ell$ produce sketches using fewer bits of storage space. We validate the method with experiments, including an application to nearest neighbor search. | https://openreview.net/pdf/0b61638bdc473a11461a84a7f80fe3487d5a6e30.pdf |
Towards Scalable and Stable Parallelization of Nonlinear RNNs | https://openreview.net/forum?id=hBCxxVQDBw | https://openreview.net/forum?id=hBCxxVQDBw | Xavier Gonzalez,Andrew Warrington,Jimmy T.H. Smith,Scott Linderman | NIPS 2024,Poster | Conventional nonlinear RNNs are not naturally parallelizable across the sequence length, unlike transformers and linear RNNs. Lim et. al. therefore tackle parallelized evaluation of nonlinear RNNs, posing it as a fixed point problem solved with Newton's method. By deriving and applying a parallelized form of Newton's method, they achieve large speedups over sequential evaluation. However, their approach inherits cubic computational complexity and numerical instability. We tackle these weaknesses. To reduce the computational complexity, we apply quasi-Newton approximations and show they converge comparably, use less memory, and are faster, compared to full-Newton. To stabilize Newton's method, we leverage a connection between Newton's method damped with trust regions and Kalman smoothing. This connection allows us to stabilize the iteration, per the trust region, and use efficient parallelized Kalman algorithms to retain performance. We compare these methods empirically and highlight use cases where each algorithm excels. | https://openreview.net/pdf/054bd98d9f6a779fbde59e5b8df033ed98c92dfd.pdf |
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better | https://openreview.net/forum?id=fNoleQa9RX | https://openreview.net/forum?id=fNoleQa9RX | Scott Geng,Cheng-Yu Hsieh,Vivek Ramanujan,Matthew Wallingford,Chun-Liang Li,Pang Wei Koh,Ranjay Krishna | NIPS 2024,Poster | Generative text-to-image models enable us to synthesize unlimited amounts of images in a controllable manner, spurring many recent efforts to train vision models with synthetic data. However, every synthetic image ultimately originates from the upstream data used to train the generator. Does the intermediate generator provide additional information over directly training on relevant parts of the upstream data?
Grounding this question in the setting of image classification, we compare finetuning on task-relevant, targeted synthetic data generated by Stable Diffusion---a generative model trained on the LAION-2B dataset---against finetuning on targeted real images retrieved directly from LAION-2B. We show that while synthetic data can benefit some downstream tasks, it is universally matched or outperformed by real data from the simple retrieval baseline. Our analysis suggests that this underperformance is partially due to generator artifacts and inaccurate task-relevant visual details in the synthetic images. Overall, we argue that targeted retrieval is a critical baseline to consider when training with synthetic data---a baseline that current methods do not yet surpass. We release code, data, and models at [https://github.com/scottgeng00/unmet-promise/](https://github.com/scottgeng00/unmet-promise). | https://openreview.net/pdf/c2284c545059ec27c6dcfc2ba711727798963d58.pdf |
A Structure-Aware Framework for Learning Device Placements on Computation Graphs | https://openreview.net/forum?id=Kzno1r3Xef | https://openreview.net/forum?id=Kzno1r3Xef | Shukai Duan,Heng Ping,Nikos Kanakaris,Xiongye Xiao,Panagiotis Kyriakis,Nesreen K. Ahmed,Peiyu Zhang,Guixiang Ma,Mihai Capotă,Shahin Nazarian,Theodore L. Willke,Paul Bogdan | NIPS 2024,Poster | Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to $58.2\%$ over CPU execution and by up to $60.24\%$ compared to other commonly used baselines. | https://openreview.net/pdf/344d24d9d6c4f7273dbdceae38b89aca712973eb.pdf |
Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models | https://openreview.net/forum?id=GNSMl1P5VR | https://openreview.net/forum?id=GNSMl1P5VR | Yushi Hu,Weijia Shi,Xingyu Fu,Dan Roth,Mari Ostendorf,Luke Zettlemoyer,Noah A. Smith,Ranjay Krishna | NIPS 2024,Poster | Humans draw to facilitate reasoning: we draw auxiliary lines when solving geometry problems; we mark and circle when reasoning on maps; we use sketches to amplify our ideas and relieve our limited-capacity working memory. However, such actions are missing in current multimodal language models (LMs). Current chain-of-thought and tool-use paradigms only use text as intermediate reasoning steps. In this work, we introduce Sketchpad, a framework that gives multimodal LMs a visual sketchpad and tools to draw on the sketchpad. The LM conducts planning and reasoning according to the visual artifacts it has drawn. Different from prior work, which uses text-to-image models to enable LMs to draw, Sketchpad enables LMs to draw with lines, boxes, marks, etc., which is closer to human sketching and better facilitates reasoning. \name can also use specialist vision models during the sketching process (e.g., draw bounding boxes with object detection models, draw masks with segmentation models), to further enhance visual perception and reasoning. We experiment on a wide range of math tasks (including geometry, functions, graph, chess) and complex visual reasoning tasks. Sketchpad substantially improves performance on all tasks over strong base models with no sketching, yielding an average gain of 12.7% on math tasks, and 8.6% on vision tasks. GPT-4o with Sketchpad sets a new state of the art on all tasks, including V*Bench (80.3%), BLINK spatial reasoning (83.9%), and visual correspondence (80.8%). We will release all code and data. | https://openreview.net/pdf/ef421114c4f5982516766e4ef464a1fe54b1b572.pdf |
Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs | https://openreview.net/forum?id=TNEmAgwoXR | https://openreview.net/forum?id=TNEmAgwoXR | Tian Tian,Lin Yang,Csaba Szepesvari | NIPS 2024,Poster | The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing cumulative reward. However, the current understanding of how to learn efficiently in a CMDP environment with a potentially infinite number of states remains under investigation, particularly when function approximation is applied to the value functions. In this paper, we address the learning problem given linear function approximation with $q_{\pi}$-realizability, where the value functions of all policies are linearly representable with a known feature map, a setting known to be more general and challenging than other linear settings. Utilizing a local-access model, we propose a novel primal-dual algorithm that, after $\tilde{O}(\text{poly}(d) \epsilon^{-3})$ iterations, outputs with high probability a policy that strictly satisfies the constraints while nearly optimizing the value with respect to a reward function. Here, $d$ is the feature dimension and $\epsilon > 0$ is a given error. The algorithm relies on a carefully crafted off-policy evaluation procedure to evaluate the policy using historical data, which informs policy updates through policy gradients and conserves samples. To our knowledge, this is the first result achieving polynomial sample complexity for CMDP in the $q_{\pi}$-realizable setting. | https://openreview.net/pdf/9c2af3f8d9c16b5729d7d8ff36f13a70315c5e0c.pdf |
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting | https://openreview.net/forum?id=Ns0LQokxa5 | https://openreview.net/forum?id=Ns0LQokxa5 | Umangi Jain,Ashkan Mirzaei,Igor Gilitschenski | NIPS 2024,Poster | We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented by interacting with a single view. It accepts intuitive user input, such as point clicks, coarse scribbles, or text. Using 3D Gaussian Splatting (3DGS) as the underlying scene representation simplifies the extraction of objects of interest which are considered to be a subset of the scene's Gaussians. Our key idea is to represent the scene as a graph and use the graph-cut algorithm to minimize an energy function to effectively partition the Gaussians into foreground and background. To achieve this, we construct a graph based on scene Gaussians and devise a segmentation-aligned energy function on the graph to combine user inputs with scene properties. To obtain an initial coarse segmentation, we leverage 2D image/video segmentation models and further refine these coarse estimates using our graph construction. Our empirical evaluations show the adaptability of GaussianCut across a diverse set of scenes. GaussianCut achieves competitive performance with state-of-the-art approaches for 3D segmentation without requiring any additional segmentation-aware training | https://openreview.net/pdf/bed9099005e28fab95397cb818fadc9488157252.pdf |
A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training | https://openreview.net/forum?id=MJgMMqMDu4 | https://openreview.net/forum?id=MJgMMqMDu4 | Jie Ji,Gen Li,Jingjing Fu,Fatemeh Afghah,Linke Guo,Xiaoyong Yuan,Xiaolong Ma | NIPS 2024,Poster | Sparse training stands as a landmark approach in addressing the considerable training resource demands imposed by the continuously expanding size of Deep Neural Networks (DNNs). However, the training of a sparse DNN encounters great challenges in achieving optimal generalization ability despite the efforts from the state-of-the-art sparse training methodologies. To unravel the mysterious reason behind the difficulty of sparse training, we connect the network sparsity with neural loss functions structure, and identify the cause of such difficulty lies in chaotic loss surface. In light of such revelation, we propose $S^{2} - SAM$, characterized by a **S**ingle-step **S**harpness_**A**ware **M**inimization that is tailored for **S**parse training. For the first time, $S^{2} - SAM$ innovates the traditional SAM-style optimization by approximating sharpness perturbation through prior gradient information, incurring *zero extra cost*. Therefore, $S^{2} - SAM$ not only exhibits the capacity to improve generalization but also aligns with the efficiency goal of sparse training. Additionally, we study the generalization result of $S^{2} - SAM$ and provide theoretical proof for convergence. Through extensive experiments, $S^{2} - SAM$ demonstrates its universally applicable plug-and-play functionality, enhancing accuracy across various sparse training methods. Code available at https://github.com/jjsrf/SSAM-NEURIPS2024. | https://openreview.net/pdf/b232744747c0917b88facb8e8dfa64df8b2665b6.pdf |
SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers | https://openreview.net/forum?id=3gvGZhkkVt | https://openreview.net/forum?id=3gvGZhkkVt | Parsa Esmati,Amirhossein Dadashzadeh,Vahid Goodarzi Ardakani,Nicolas Larrosa,Nicolò Grilli | NIPS 2024,Poster | Current approaches using sequential networks have shown promise in estimating field variables for dynamical systems, but they are often limited by high rollout errors. The unresolved issue of rollout error accumulation results in unreliable estimations as the network predicts further into the future, with each step's error compounding and leading to an increase in inaccuracy. Here, we introduce the State-Exchange Attention (SEA) module, a novel transformer-based module enabling information exchange between encoded fields through multi-head cross-attention. The cross-field multidirectional information exchange design enables all state variables in the system to exchange information with one another, capturing physical relationships and symmetries between fields. Additionally, we introduce an efficient ViT-like mesh autoencoder to generate spatially coherent mesh embeddings for a large number of meshing cells. The SEA integrated transformer demonstrates the state-of-the-art rollout error compared to other competitive baselines. Specifically, we outperform PbGMR-GMUS Transformer-RealNVP and GMR-GMUS Transformer, with a reduction in error of 88% and 91%, respectively. Furthermore, we demonstrate that the SEA module alone can reduce errors by 97% for state variables that are highly dependent on other states of the system. The repository for this work is available at: https://github.com/ParsaEsmati/SEA | https://openreview.net/pdf/899aa844132a7590d0fd4ff1ca9b65a86a8cbb57.pdf |
S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity | https://openreview.net/forum?id=lEUle8S4xQ | https://openreview.net/forum?id=lEUle8S4xQ | Xinyu Yang,Jixuan Leng,Geyang Guo,Jiawei Zhao,Ryumei Nakada,Linjun Zhang,Huaxiu Yao,Beidi Chen | NIPS 2024,Poster | Current PEFT methods for LLMs can achieve high quality, efficient training, or scalable serving, but not all three simultaneously.
To address this limitation, we investigate sparse fine-tuning and observe a remarkable improvement in generalization ability.
Utilizing this key insight, we propose a family of Structured Sparse Fine-Tuning (S${^2}$FT) methods for LLMs, which concurrently achieve state-of-the-art fine-tuning performance, training efficiency, and inference scalability. S${^2}$FT accomplishes this by "selecting sparsely and computing densely". Based on the coupled structures in LLMs, \model selects a few attention heads and channels in the MHA and FFN modules for each Transformer block, respectively. Next, it co-permutes the weight matrices on both sides of all coupled structures to connect the selected subsets in each layer into a dense submatrix. Finally, S${^2}$FT performs in-place gradient updates on all selected submatrices.
Through theoretical analyses and empirical results, our method prevents forgetting while simplifying optimization, delivers SOTA performance on both commonsense and arithmetic reasoning with 4.6% and 1.3% average improvements compared to LoRA, and surpasses full FT by 11.5% when generalizing to various domains after instruction tuning.
Using our partial back-propagation algorithm, S${^2}$FT saves training memory up to 3$\times$ and improves latency by 1.5-2.7$\times$ compared to full FT, while achieving an average 10\% improvement over LoRA on both metrics. We further demonstrate that the weight updates in S${^2}$FT can be decoupled into adapters, enabling effective fusion, fast switch, and efficient parallelism when serving multiple fine-tuned models. | https://openreview.net/pdf/4471079851bf7b34b21b69c2d29905ec2566b2ba.pdf |
Your contrastive learning problem is secretly a distribution alignment problem | https://openreview.net/forum?id=iNUKoLU8xb | https://openreview.net/forum?id=iNUKoLU8xb | Zihao Chen,Chi-Heng Lin,Ran Liu,Jingyun Xiao,Eva L Dyer | NIPS 2024,Poster | Despite the success of contrastive learning (CL) in vision and language, its theoretical foundations and mechanisms for building representations remain poorly understood. In this work, we build connections between noise contrastive estimation losses widely used in CL and distribution alignment with entropic optimal transport (OT). This connection allows us to develop a family of different losses and multistep iterative variants for existing CL methods. Intuitively, by using more information from the distribution of latents, our approach allows a more distribution-aware manipulation of the relationships within augmented sample sets.
We provide theoretical insights and experimental evidence demonstrating the benefits of our approach for generalized contrastive alignment. Through this framework, it is possible to leverage tools in OT to build unbalanced losses to handle noisy views and customize the representation space by changing the constraints on alignment.
By reframing contrastive learning as an alignment problem and leveraging existing optimization tools for OT, our work provides new insights and connections between different self-supervised learning models in addition to new tools that can be more easily adapted to incorporate domain knowledge into learning. | https://openreview.net/pdf/c7c6656445f089a46f221ffe1c65abf802905470.pdf |
Data Free Backdoor Attacks | https://openreview.net/forum?id=pX71TM2MLh | https://openreview.net/forum?id=pX71TM2MLh | Bochuan Cao,Jinyuan Jia,Chuxuan Hu,Wenbo Guo,Zhen Xiang,Jinghui Chen,Bo Li,Dawn Song | NIPS 2024,Poster | Backdoor attacks aim to inject a backdoor into a classifier such that it predicts any input with an attacker-chosen backdoor trigger as an attacker-chosen target class.
Existing backdoor attacks require either retraining the classifier with some clean data or modifying the model's architecture.
As a result, they are 1) not applicable when clean data is unavailable, 2) less efficient when the model is large, and 3) less stealthy due to architecture changes.
In this work, we propose DFBA, a novel retraining-free and data-free backdoor attack without changing the model architecture.
Technically, our proposed method modifies a few parameters of a classifier to inject a backdoor.
Through theoretical analysis, we verify that our injected backdoor is provably undetectable and unremovable by various state-of-the-art defenses under mild assumptions.
Our evaluation on multiple datasets further demonstrates that our injected backdoor: 1) incurs negligible classification loss, 2) achieves 100\% attack success rates, and 3) bypasses six existing state-of-the-art defenses.
Moreover, our comparison with a state-of-the-art non-data-free backdoor attack shows our attack is more stealthy and effective against various defenses while achieving less classification accuracy loss.
We will release our code upon paper acceptance. | https://openreview.net/pdf/626a446188f0b3dac28d22823764a7655735a226.pdf |
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation | https://openreview.net/forum?id=Aj0Zf28l6o | https://openreview.net/forum?id=Aj0Zf28l6o | Jiwoong Park,Yang Shen | NIPS 2024,Poster | How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world?
In this paper, we address the question by focusing on a fundamental biochemical problem of generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner.
Our approach consists of two hierarchical stages: i) generation of coarse-grained fragment-level 3D structure from the molecular graph, and ii) generation of fine atomic details from the coarse-grained approximated structure while allowing the latter to be adjusted simultaneously.
For the challenging second stage, which demands preserving coarse-grained information while ensuring SE(3) equivariance, we introduce a novel generative model termed Equivariant Blurring Diffusion (EBD), which defines a forward process that moves towards the fragment-level coarse-grained structure by blurring the fine atomic details of conformers, and a reverse process that performs the opposite operation using equivariant networks.
We demonstrate the effectiveness of EBD by geometric and chemical comparison to state-of-the-art denoising diffusion models on a benchmark of drug-like molecules.
Ablation studies draw insights on the design of EBD by thoroughly analyzing its architecture, which includes the design of the loss function and the data corruption process.
Codes are released at https://github.com/Shen-Lab/EBD. | https://openreview.net/pdf/18ad6fd7ccc4d430e351d06384b59c3ba44fe1a4.pdf |
UNIT: Unifying Image and Text Recognition in One Vision Encoder | https://openreview.net/forum?id=YIxKeHQZpi | https://openreview.net/forum?id=YIxKeHQZpi | Yi Zhu,Zhou Yanpeng,Chunwei Wang,Yang Cao,Jianhua Han,Lu Hou,Hang Xu | NIPS 2024,Poster | Currently, vision encoder models like Vision Transformers (ViTs) typically excel at image recognition tasks but cannot simultaneously support text recognition like human visual recognition. To address this limitation, we propose UNIT, a novel training framework aimed at UNifying Image and Text recognition within a single model. Starting with a vision encoder pre-trained with image recognition tasks, UNIT introduces a lightweight language decoder for predicting text outputs and a lightweight vision decoder to prevent catastrophic forgetting of the original image encoding capabilities. The training process comprises two stages: intra-scale pretraining and inter-scale finetuning. During intra-scale pretraining, UNIT learns unified representations from multi-scale inputs, where images and documents are at their commonly used resolution, to enable fundamental recognition capability. In the inter-scale finetuning stage, the model introduces scale-exchanged data, featuring images and documents at resolutions different from the most commonly used ones, to enhance its scale robustness. Notably, UNIT retains the original vision encoder architecture, making it cost-free in terms of inference and deployment. Experiments across multiple benchmarks confirm that our method significantly outperforms existing methods on document-related tasks (e.g., OCR and DocQA) while maintaining the performances on natural images, demonstrating its ability to substantially enhance text recognition without compromising its core image recognition capabilities. | https://openreview.net/pdf/1a1619bf7aa98f58a0486c2329bc820c108fbdea.pdf |
Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification | https://openreview.net/forum?id=UXuBzWoZGK | https://openreview.net/forum?id=UXuBzWoZGK | Thomas Kwa,Drake Thomas,Adrià Garriga-Alonso | NIPS 2024,Poster | When applying reinforcement learning from human feedback (RLHF), the reward is learned from data and, therefore, always has some error. It is common to mitigate this by regularizing the policy with KL divergence from a base model, with the hope that balancing reward with regularization will achieve desirable outcomes despite this reward misspecification. We show that when the reward function has light-tailed error, optimal policies under less restrictive KL penalties achieve arbitrarily high utility. However, if error is heavy-tailed, some policies obtain arbitrarily high reward despite achieving no more utility than the base model--a phenomenon we call catastrophic Goodhart. We adapt a discrete optimization method to measure the tails of reward models, finding that they are consistent with light-tailed error. However, the pervasiveness of heavy-tailed distributions in many real-world applications indicates that future sources of RL reward could have heavy-tailed error, increasing the likelihood of reward hacking even with KL regularization. | https://openreview.net/pdf/be3a14bb23f805713d5c57d8b5458c2712757e8c.pdf |
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes | https://openreview.net/forum?id=OF0YsxoRai | https://openreview.net/forum?id=OF0YsxoRai | Yunyue Wei,Vincent Zhuang,Saraswati Soedarmadji,Yanan Sui | NIPS 2024,Poster | Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic complexity of computing the Gaussian process (GP) surrogate. While various approximate GP models have been employed to scale Bayesian optimization to larger sample sizes, most suffer from overly-smooth estimation and focus primarily on problems that allow for large online samples. In this work, we argue that Bayesian optimization algorithms with sparse GPs can more efficiently allocate their representational power to relevant regions of the search space. To achieve this, we propose focalized GP, which leverages a novel variational loss function to achieve stronger local prediction, as well as FocalBO, which hierarchically optimizes the focalized GP acquisition function over progressively smaller search spaces. Experimental results demonstrate that FocalBO can efficiently leverage large amounts of offline and online data to achieve state-of-the-art performance on robot morphology design and to control a 585-dimensional musculoskeletal system. | https://openreview.net/pdf/3b2fc5597a3c16b9823e4dcaa2fc4a20f006d647.pdf |
Transfer Learning for Latent Variable Network Models | https://openreview.net/forum?id=PK8xOCBQRO | https://openreview.net/forum?id=PK8xOCBQRO | Akhil Jalan,Arya Mazumdar,Soumendu Sundar Mukherjee,Purnamrita Sarkar | NIPS 2024,Poster | We study transfer learning for estimation in latent variable network models. In our setting, the conditional edge probability matrices given the latent variables are represented by $P$ for the source and $Q$ for the target. We wish to estimate $Q$ given two kinds of data: (1) edge data from a subgraph induced by an $o(1)$ fraction of the nodes of $Q$, and (2) edge data from all of $P$. If the source $P$ has no relation to the target $Q$, the estimation error must be $\Omega(1)$. However, we show that if the latent variables are shared, then vanishing error is possible. We give an efficient algorithm that utilizes the ordering of a suitably defined graph distance. Our algorithm achieves $o(1)$ error and does not assume a parametric form on the source or target networks. Next, for the specific case of Stochastic Block Models we prove a minimax lower bound and show that a simple algorithm achieves this rate. Finally, we empirically demonstrate our algorithm's use on real-world and simulated graph transfer problems. | https://openreview.net/pdf/4830f6ed817c8346d73ee25f85017b31d6e4996f.pdf |
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning | https://openreview.net/forum?id=QB6CvDqa6b | https://openreview.net/forum?id=QB6CvDqa6b | Qian Lin,Zongkai Liu,Danying Mo,Chao Yu | NIPS 2024,Poster | In recent years, significant progress has been made in multi-objective reinforcement learning (RL) research, which aims to balance multiple objectives by incorporating preferences for each objective. In most existing studies, specific preferences must be provided during deployment to indicate the desired policies explicitly. However, designing these preferences depends heavily on human prior knowledge, which is typically obtained through extensive observation of high-performing demonstrations with expected behaviors. In this work, we propose a simple yet effective offline adaptation framework for multi-objective RL problems without assuming handcrafted target preferences, but only given several demonstrations to implicitly indicate the preferences of expected policies. Additionally, we demonstrate that our framework can naturally be extended to meet constraints on safety-critical objectives by utilizing safe demonstrations, even when the safety thresholds are unknown. Empirical results on offline multi-objective and safe tasks demonstrate the capability of our framework to infer policies that align with real preferences while meeting the constraints implied by the provided demonstrations. | https://openreview.net/pdf/7cb3958531c21777dd36e0b964c92ade366fc766.pdf |
Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling | https://openreview.net/forum?id=Mmcy1p15Hc | https://openreview.net/forum?id=Mmcy1p15Hc | Wei Tang,Haifeng Xu,Ruimin Zhang,Derek Zhu | NIPS 2024,Poster | Prophet inequality concerns a basic optimal stopping problem and states that simple threshold stopping policies --- i.e., accepting the first reward larger than a certain threshold --- can achieve tight $\frac{1}{2}$-approximation to the optimal prophet value. Motivated by its economic applications, this paper studies the robustness of this approximation to natural strategic manipulations in which each random reward is associated with a self-interested player who may selectively reveal his realized reward to the searcher in order to maximize his probability of being selected.
We say a threshold policy is $\alpha$(-strategically)-robust if it (a) achieves the $\alpha$-approximation to the prophet value for strategic players; and (b) meanwhile remains a $\frac{1}{2}$-approximation in the standard non-strategic setting.
Starting with a characterization of each player's optimal information revealing strategy, we demonstrate the intrinsic robustness of prophet inequalities to strategic reward signaling through the following results:
(1) for arbitrary reward distributions, there is a threshold policy that is $\frac{1-\frac{1}{e}}{2}$-robust, and this ratio is tight;
(2) for i.i.d. reward distributions, there is a threshold policy that is $\frac{1}{2}$-robust, which is tight for the setting;
and (3) for log-concave (but non-identical) reward distributions, the $\frac{1}{2}$-robustness can also be achieved under certain regularity assumptions. | https://openreview.net/pdf/5dda2558f65809779f2ce50bbc3fac437cf946ba.pdf |
Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization | https://openreview.net/forum?id=Prw98p1nV0 | https://openreview.net/forum?id=Prw98p1nV0 | Mingwei Xu,Xiaofeng Cao,Ivor Tsang | NIPS 2024,Poster | Teaching is a potentially effective approach for understanding interactions among multiple intelligences. Previous explorations have convincingly shown that teaching presents additional opportunities for observation and demonstration within the learning model, such as data distillation and selection. However, the underlying optimization principles and convergence of interactive teaching lack theoretical analysis, and in this regard co-teaching serves as a notable prototype. In this paper, we discuss its role as a reduction of the larger loss landscape derived from Sharpness-Aware Minimization (SAM). Then, we classify it as an iterative parameter estimation process using Expectation-Maximization. The convergence of this typical interactive teaching is achieved by continuously optimizing a variational lower bound on the log marginal likelihood. This lower bound represents the expected value of the log posterior distribution of the latent variables under a scaled, factorized variational distribution. To further enhance interactive teaching's performance, we incorporate SAM's strong generalization information into interactive teaching, referred as Sharpness Reduction Interactive Teaching (SRIT). This integration can be viewed as a novel sequential optimization process. Finally, we validate the performance of our approach through multiple experiments. | https://openreview.net/pdf/98ade1fd990a7c5d2b7c8f5c6e8677030fa52c6a.pdf |
Policy-shaped prediction: avoiding distractions in model-based reinforcement learning | https://openreview.net/forum?id=hgdh4foghu | https://openreview.net/forum?id=hgdh4foghu | Miles Richard Hutson,Isaac Kauvar,Nick Haber | NIPS 2024,Poster | Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods ---including DreamerV3 and DreamerPro--- with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through a synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning. | https://openreview.net/pdf/85986bb7f2dd538aa4e129bdca1ba0ffe4b2e6f3.pdf |
Recognize Any Regions | https://openreview.net/forum?id=qKfiWNHp6k | https://openreview.net/forum?id=qKfiWNHp6k | Haosen Yang,Chuofan Ma,Bin Wen,Yi Jiang,Zehuan Yuan,Xiatian Zhu | NIPS 2024,Poster | Understanding the semantics of individual regions or patches of unconstrained images, such as open-world object detection, remains a critical yet challenging task in computer vision. Building on the success of powerful image-level vision-language (ViL) foundation models like CLIP, recent efforts have sought to harness their capabilities by either training a contrastive model from scratch with an extensive collection of region-label pairs or aligning the outputs of a detection model with image-level representations of region proposals. Despite notable progress, these approaches are plagued by computationally intensive training requirements, susceptibility to data noise,
and deficiency in contextual information. To address these limitations, we explore the synergistic potential of off-the-shelf foundation models, leveraging their respective strengths in localization and semantics. We introduce a novel, generic, and efficient architecture, named RegionSpot, designed to integrate position-aware localization knowledge from a localization foundation model (e.g., SAM) with semantic information from a ViL model (e.g., CLIP). To fully exploit pretrained knowledge while minimizing training overhead, we keep both foundation models frozen, focusing optimization efforts solely on a lightweight attention-based knowledge integration module.
Extensive experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives, along with substantial computational savings (e.g., training our model with 3 million data in a single day using 8 V100 GPUs).
RegionSpot outperforms GLIP-L by 2.9 in mAP on LVIS val set, with an even larger margin of 13.1 AP for more challenging and rare categories, and a 2.5 AP increase on ODinW. Furthermore, it exceeds GroundingDINO-L by 11.0 AP for rare categories on the LVIS minival set. | https://openreview.net/pdf/e158974032bb53bfca7244c8a0e2c67406654e37.pdf |
Oracle-Efficient Reinforcement Learning for Max Value Ensembles | https://openreview.net/forum?id=KLL70pTQ17 | https://openreview.net/forum?id=KLL70pTQ17 | Marcel Hussing,Michael Kearns,Aaron Roth,Sikata Bela Sengupta,Jessica Sorrell | NIPS 2024,Poster | Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardinality) and experimentally (where function approximation and policy gradient techniques often scale poorly and suffer from instability and high variance). One line of research attempting to address these difficulties
makes the natural assumption that we are given a collection of base or *constituent* policies (possibly heuristic) upon which we would like to improve in a scalable manner. In this work we aim to compete with the *max-following policy*, which at each state follows the action of whichever constituent policy has the highest value. The max-following policy is always at least as good as the best constituent policy, and may be considerably better. Our main result is an efficient algorithm that learns to compete with the max-following policy, given only access to the constituent policies (but not their value functions). In contrast to prior work in similar settings, our theoretical results require only the minimal assumption of an ERM oracle for value function approximation for the constituent policies (and not the global optimal policy or the max-following policy itself) on samplable distributions. We illustrate our algorithm's experimental effectiveness and behavior on several robotic simulation testbeds. | https://openreview.net/pdf/7b8153019b588df4f07fea0a2e38cf08689af1e8.pdf |
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection | https://openreview.net/forum?id=EdXW71LvKE | https://openreview.net/forum?id=EdXW71LvKE | Jisong Kim,Minjae Seong,Jun Won Choi | NIPS 2024,Poster | Accurate and robust 3D object detection is a critical component in autonomous vehicles and robotics. While recent radar-camera fusion methods have made significant progress by fusing information in the bird's-eye view (BEV) representation, they often struggle to effectively capture the motion of dynamic objects, leading to limited performance in real-world scenarios. In this paper, we introduce CRT-Fusion, a novel framework that integrates temporal information into radar-camera fusion to address this challenge. Our approach comprises three key modules: Multi-View Fusion (MVF), Motion Feature Estimator (MFE), and Motion Guided Temporal Fusion (MGTF). The MVF module fuses radar and image features within both the camera view and bird's-eye view, thereby generating a more precise unified BEV representation. The MFE module conducts two simultaneous tasks: estimation of pixel-wise velocity information and BEV segmentation. Based on the velocity and the occupancy score map obtained from the MFE module, the MGTF module aligns and fuses feature maps across multiple timestamps in a recurrent manner. By considering the motion of dynamic objects, CRT-Fusion can produce robust BEV feature maps, thereby improving detection accuracy and robustness. Extensive evaluations on the challenging nuScenes dataset demonstrate that CRT-Fusion achieves state-of-the-art performance for radar-camera-based 3D object detection. Our approach outperforms the previous best method in terms of NDS by +1.7%, while also surpassing the leading approach in mAP by +1.4%. These significant improvements in both metrics showcase the effectiveness of our proposed fusion strategy in enhancing the reliability and accuracy of 3D object detection. | https://openreview.net/pdf/4324eb5080025d064864306ccd9a5422f55d18ca.pdf |
On the Surprising Effectiveness of Attention Transfer for Vision Transformers | https://openreview.net/forum?id=5DwqmoCE1N | https://openreview.net/forum?id=5DwqmoCE1N | Alexander Cong Li,Yuandong Tian,Beidi Chen,Deepak Pathak,Xinlei Chen | NIPS 2024,Poster | Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations. Is this actually true? We investigate this question and find that the features and representations learned during pre-training are not essential. Surprisingly, using only the attention patterns from pre-training (i.e., guiding how information flows between tokens) is sufficient for models to learn high quality features from scratch and achieve comparable downstream performance. We show this by introducing a simple method called attention transfer, where only the attention patterns from a pre-trained teacher ViT are transferred to a student, either by copying or distilling the attention maps. Since attention transfer lets the student learn its own features, ensembling it with a fine-tuned teacher also further improves accuracy on ImageNet. We systematically study various aspects of our findings on the sufficiency of attention maps, including distribution shift settings where they underperform fine-tuning. We hope our exploration provides a better understanding of what pre-training accomplishes and leads to a useful alternative to the standard practice of fine-tuning. | https://openreview.net/pdf/3cb6ab79a8ac05e29b69c4600053fd98fe84f7f2.pdf |
A Canonicalization Perspective on Invariant and Equivariant Learning | https://openreview.net/forum?id=jjcY92FX4R | https://openreview.net/forum?id=jjcY92FX4R | George Ma,Yifei Wang,Derek Lim,Stefanie Jegelka,Yisen Wang | NIPS 2024,Poster | In many applications, we desire neural networks to exhibit invariance or equivariance to certain groups due to symmetries inherent in the data. Recently, frame-averaging methods emerged to be a unified framework for attaining symmetries efficiently by averaging over input-dependent subsets of the group, i.e., frames. What we currently lack is a principled understanding of the design of frames. In this work, we introduce a canonicalization perspective that provides an essential and complete view of the design of frames. Canonicalization is a classic approach for attaining invariance by mapping inputs to their canonical forms. We show that there exists an inherent connection between frames and canonical forms. Leveraging this connection, we can efficiently compare the complexity of frames as well as determine the optimality of certain frames. Guided by this principle, we design novel frames for eigenvectors that are strictly superior to existing methods --- some are even optimal --- both theoretically and empirically. The reduction to the canonicalization perspective further uncovers equivalences between previous methods. These observations suggest that canonicalization provides a fundamental understanding of existing frame-averaging methods and unifies existing equivariant and invariant learning methods. Code is available at https://github.com/PKU-ML/canonicalization. | https://openreview.net/pdf/6fdab7559bff9da67419fc255751d38598a58ae7.pdf |
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions | https://openreview.net/forum?id=i816TeqgVh | https://openreview.net/forum?id=i816TeqgVh | Zizhao Wang,Jiaheng Hu,Caleb Chuck,Stephen Chen,Roberto Martín-Martín,Amy Zhang,Scott Niekum,Peter Stone | NIPS 2024,Poster | Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free interactions with environments. Existing unsupervised skill discovery methods learn skills by encouraging distinguishable behaviors that cover diverse states. However, in complex environments with many state factors (e.g., household environments with many objects), learning skills that cover all possible states is impossible, and naively encouraging state diversity often leads to simple skills that are not ideal for solving downstream tasks. This work introduces Skill Discovery from Local Dependencies (SkiLD), which leverages state factorization as a natural inductive bias to guide the skill learning process. The key intuition guiding SkiLD is that skills that induce \textbf{diverse interactions} between state factors are often more valuable for solving downstream tasks. To this end, SkiLD develops a novel skill learning objective that explicitly encourages the mastering of skills that effectively induce different interactions within an environment. We evaluate SkiLD in several domains with challenging, long-horizon sparse reward tasks including a realistic simulated household robot domain, where SkiLD successfully learns skills with clear semantic meaning and shows superior performance compared to existing unsupervised reinforcement learning methods that only maximize state coverage. | https://openreview.net/pdf/02c32e154257dc57f63f03ad314a8242d5f3dbdb.pdf |
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead | https://openreview.net/forum?id=dAXuir2ets | https://openreview.net/forum?id=dAXuir2ets | Minsu Kim,Walid Saad,Merouane Abdelkader DEBBAH,Choong Seon Hong | NIPS 2024,Poster | The large communication and computation overhead of federated learning (FL) is one of the main challenges facing its practical deployment over resource-constrained clients and systems. In this work, SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. In SpaFL, a trainable threshold is defined for each filter/neuron to prune its all connected
parameters, thereby leading to structured sparsity. To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters, thereby learning how to prune. Further, global thresholds are used to update model parameters by extracting aggregated parameter importance. The generalization bound of SpaFL is also derived, thereby proving key insights on the relation between sparsity and performance. Experimental results show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines. The code is available at https://github.com/news-vt/SpaFL_NeruIPS_2024 | https://openreview.net/pdf/74429405f3d0b3fdbe3bf31f9b09e1ea523d149d.pdf |
UniAR: A Unified model for predicting human Attention and Responses on visual content | https://openreview.net/forum?id=FjssnGuHih | https://openreview.net/forum?id=FjssnGuHih | Peizhao Li,Junfeng He,Gang Li,Rachit Bhargava,Shaolei Shen,NACHIAPPAN VALLIAPPAN,Youwei Liang,Hongxiang Gu,Venky Ramachandran,Golnaz farhadi,Yang Li,Kai J Kohlhoff,Vidhya Navalpakkam | NIPS 2024,Poster | Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subjective preferences or likes. Yet most prior research has focused on modeling implicit and explicit human behavior in isolation; and often limited to a specific type of visual content. We propose UniAR -- a unified model of human attention and preference behavior across diverse visual content. UniAR leverages a multimodal transformer to predict subjective feedback, such as satisfaction or aesthetic quality, along with the underlying human attention or interaction heatmaps and viewing order. We train UniAR on diverse public datasets spanning natural images, webpages, and graphic designs, and achieve SOTA performance on multiple benchmarks across various image domains and behavior modeling tasks. Potential applications include providing instant feedback on the effectiveness of UIs/visual content, and enabling designers and content-creation models to optimize their creation for human-centric improvements. | https://openreview.net/pdf/3bcbe97b1b4b7ebce3be6038936511932cdd80e9.pdf |
Hypothesis Testing the Circuit Hypothesis in LLMs | https://openreview.net/forum?id=5ai2YFAXV7 | https://openreview.net/forum?id=5ai2YFAXV7 | Claudia Shi,Nicolas Beltran-Velez,Achille Nazaret,Carolina Zheng,Adrià Garriga-Alonso,Andrew Jesson,Maggie Makar,David Blei | NIPS 2024,Poster | Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented.
One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. But how can we evaluate this hypothesis?
In this paper, we formalize a set of criteria that a circuit is hypothesized to meet and develop a suite of hypothesis tests to evaluate how well circuits satisfy them.
The criteria focus on the extent to which the LLM's behavior is preserved, the degree of localization of this behavior, and whether the circuit is minimal.
We apply these tests to six circuits described in the research literature.
We find that synthetic circuits -- circuits that are hard-coded in the model -- align with the idealized properties.
Circuits discovered in Transformer models satisfy the criteria to varying degrees.
To facilitate future empirical studies of circuits, we created the \textit{circuitry} package, a wrapper around the \textit{TransformerLens} library, which abstracts away lower-level manipulations of hooks and activations. The software is available at \url{https://github.com/blei-lab/circuitry}. | https://openreview.net/pdf/d42b43708ca0c06c98f6b5d7a422bd9082f54bdf.pdf |
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable | https://openreview.net/forum?id=i4gqCM1r3z | https://openreview.net/forum?id=i4gqCM1r3z | Martin Andres Bertran,Shuai Tang,Michael Kearns,Jamie Heather Morgenstern,Aaron Roth,Steven Wu | NIPS 2024,Poster | Machine unlearning is motivated by principles of data autonomy. The premise is that a person can request to have their data's influence removed from deployed models, and those models should be updated as if they were retrained without the person's data. We show that these updates expose individuals to high-accuracy reconstruction attacks which allow the attacker to recover their data in its entirety, even when the original models are so simple that privacy risk might not otherwise have been a concern. We show how to mount a near-perfect attack on the deleted data point from linear regression models. We then generalize our attack to other loss functions and architectures, and empirically demonstrate the effectiveness of our attacks across a wide range of datasets (capturing both tabular and image data). Our work highlights that privacy risk is significant even for extremely simple model classes when individuals can request deletion of their data from the model. | https://openreview.net/pdf/cb154ee097ce8ebc2a66d1f24af8d3eccfdb05a9.pdf |
Explaining Datasets in Words: Statistical Models with Natural Language Parameters | https://openreview.net/forum?id=u5BkOgWWZW | https://openreview.net/forum?id=u5BkOgWWZW | Ruiqi Zhong,Heng Wang,Dan Klein,Jacob Steinhardt | NIPS 2024,Poster | To make sense of massive data, we often first fit simplified models and then interpret the parameters; for example, we cluster the text embeddings and then interpret the mean parameters of each cluster.
However, these parameters are often high-dimensional and hard to interpret.
To make model parameters directly interpretable, we introduce a family of statistical models---including clustering, time series, and classification models---parameterized by *natural language predicates*.
For example, a cluster of text about COVID could be parameterized by the predicate ``*discusses COVID*''.
To learn these statistical models effectively, we develop a model-agnostic algorithm that optimizes continuous relaxations of predicate parameters with gradient descent and discretizes them by prompting language models (LMs).
Finally, we apply our framework to a wide range of problems: taxonomizing user chat dialogues, characterizing how they evolve across time, finding categories where one language model is better than the other, clustering math problems based on subareas, and explaining visual features in memorable images.
Our framework is highly versatile, applicable to both textual and visual domains, can be easily steered to focus on specific properties (e.g. subareas), and explains sophisticated concepts that classical methods (e.g. n-gram analysis) struggle to produce. | https://openreview.net/pdf/6bf57dd60959ba5605f358a7a7b884d7a32c7de5.pdf |
Faster Algorithms for User-Level Private Stochastic Convex Optimization | https://openreview.net/forum?id=hNlk9cIGo9 | https://openreview.net/forum?id=hNlk9cIGo9 | Andrew Lowy,Daogao Liu,Hilal Asi | NIPS 2024,Poster | We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are $n$ users (e.g., cell phones), each possessing $m$ data items (e.g., text messages), and we need to protect the privacy of each user's entire collection of data items. Existing algorithms for user-level DP SCO are impractical in many large-scale machine learning scenarios because: (i) they make restrictive assumptions on the smoothness parameter of the loss function and require the number of users to grow polynomially with the dimension of the parameter space; or (ii) they are prohibitively slow, requiring at least $(mn)^{3/2}$ gradient computations for smooth losses and $(mn)^3$ computations for non-smooth losses. To address these limitations, we provide novel user-level DP algorithms with state-of-the-art excess risk and runtime guarantees, without stringent assumptions. First, we develop a linear-time algorithm with state-of-the-art excess risk (for a non-trivial linear-time algorithm) under a mild smoothness assumption. Our second algorithm applies to arbitrary smooth losses and achieves optimal excess risk in $\approx (mn)^{9/8}$ gradient computations. Third, for non-smooth loss functions, we obtain optimal excess risk in $n^{11/8} m^{5/4}$ gradient computations. Moreover, our algorithms do not require the number of users to grow polynomially with the dimension. | https://openreview.net/pdf/f36c6f68a0ef60ef7d864db1e2e045e4ca2b6e24.pdf |
Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings | https://openreview.net/forum?id=Tj5wJslj0R | https://openreview.net/forum?id=Tj5wJslj0R | Milad Khademi Nori,IL MIN KIM | NIPS 2024,Poster | In class-incremental learning (class-IL), models must classify all previously seen classes at test time without task-IDs, leading to task confusion. Despite being a key challenge, task confusion lacks a theoretical understanding. We present a novel mathematical framework for class-IL and prove the Infeasibility Theorem, showing optimal class-IL is impossible with discriminative modeling due to task confusion. However, we establish the Feasibility Theorem, demonstrating that generative modeling can achieve optimal class-IL by overcoming task confusion. We then assess popular class-IL strategies, including regularization, bias-correction, replay, and generative classifier, using our framework. Our analysis suggests that adopting generative modeling, either for generative replay or direct classification (generative classifier), is essential for optimal class-IL. | https://openreview.net/pdf/57bd4f3d82cf6a55dece2ea557e36fce58d61778.pdf |
Boundary Decomposition for Nadir Objective Vector Estimation | https://openreview.net/forum?id=f829mkQMUg | https://openreview.net/forum?id=f829mkQMUg | Ruihao Zheng,Zhenkun Wang | NIPS 2024,Poster | The nadir objective vector plays a key role in solving multi-objective optimization problems (MOPs), where it is often used to normalize the objective space and guide the search. The current methods for estimating the nadir objective vector perform effectively only on specific MOPs. This paper reveals the limitations of these methods: exact methods can only work on discrete MOPs, while heuristic methods cannot deal with the MOP with a complicated feasible objective region. To fill this gap, we propose a general and rigorous method, namely boundary decomposition for nadir objective vector estimation (BDNE). BDNE scalarizes the MOP into a set of boundary subproblems. By utilizing bilevel optimization, boundary subproblems are optimized and adjusted alternately, thereby refining their optimal solutions to align with the nadir objective vector. We prove that the bilevel optimization identifies the nadir objective vector under mild conditions. We compare BDNE with existing methods on various black-box MOPs. The results conform to the theoretical analysis and show the significant potential of BDNE for real-world application. | https://openreview.net/pdf/ac319a1318380908cd485270961ab07f92d9d9f3.pdf |
OSLO: One-Shot Label-Only Membership Inference Attacks | https://openreview.net/forum?id=ZJBBeyEAyX | https://openreview.net/forum?id=ZJBBeyEAyX | Yuefeng Peng,Jaechul Roh,Subhransu Maji,Amir Houmansadr | NIPS 2024,Poster | We introduce One-Shot Label-Only (OSLO) membership inference attacks (MIAs), which accurately infer a given sample's membership in a target model's training set with high precision using just a single query, where the target model only returns the predicted hard label.
This is in contrast to state-of-the-art label-only attacks which require $\sim6000$ queries, yet get attack precisions lower than OSLO's.
OSLO leverages transfer-based black-box adversarial attacks. The core idea is that a member sample exhibits more resistance to adversarial perturbations than a non-member. We compare OSLO against state-of-the-art label-only attacks and demonstrate that, despite requiring only one query, our method significantly outperforms previous attacks in terms of precision and true positive rate (TPR) under the same false positive rates (FPR). For example, compared to previous label-only MIAs, OSLO achieves a TPR that is at least 7$\times$ higher under a 1\% FPR and at least 22$\times$ higher under a 0.1\% FPR on CIFAR100 for a ResNet18 model. We evaluated multiple defense mechanisms against OSLO. | https://openreview.net/pdf/9fd3b4434a639fc69ae4bba2b397013f29cbf2df.pdf |
End-To-End Causal Effect Estimation from Unstructured Natural Language Data | https://openreview.net/forum?id=gzQARCgIsI | https://openreview.net/forum?id=gzQARCgIsI | Nikita Dhawan,Leonardo Cotta,Karen Ullrich,Rahul Krishnan,Chris J. Maddison | NIPS 2024,Poster | Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions. This increases both the cost and time-to-completion for studies. We show how large, diverse observational text data can be mined with large language models (LLMs) to produce inexpensive causal effect estimates under appropriate causal assumptions. We introduce _NATURAL_, a novel family of causal effect estimators built with LLMs that operate over datasets of unstructured text. Our estimators use LLM conditional distributions (over variables of interest, given the text data) to assist in the computation of classical estimators of causal effect. We overcome a number of technical challenges to realize this idea, such as automating data curation and using LLMs to impute missing information. We prepare six (two synthetic and four real) observational datasets, paired with corresponding ground truth in the form of randomized trials, which we used to systematically evaluate each step of our pipeline. NATURAL estimators demonstrate remarkable performance, yielding causal effect estimates that fall within 3 percentage points of their ground truth counterparts, including on real-world Phase 3/4 clinical trials. Our results suggest that unstructured text data is a rich source of causal effect information, and NATURAL is a first step towards an automated pipeline to tap this resource. | https://openreview.net/pdf/3b51533646d3d910f744e6b3f9388df0f917b423.pdf |
Semidefinite Relaxations of the Gromov-Wasserstein Distance | https://openreview.net/forum?id=rM3FFH1mqk | https://openreview.net/forum?id=rM3FFH1mqk | Junyu Chen,Binh Nguyen,Shang Hui Koh,Yong Sheng Soh | NIPS 2024,Poster | The Gromov-Wasserstein (GW) distance is an extension of the optimal transport problem that allows one to match objects between incomparable spaces. At its core, the GW distance is specified as the solution of a non-convex quadratic program and is not known to be tractable to solve. In particular, existing solvers for the GW distance are only able to find locally optimal solutions. In this work, we propose a semi-definite programming (SDP) relaxation of the GW distance. The relaxation can be viewed as the Lagrangian dual of the GW distance augmented with constraints that relate to the linear and quadratic terms of transportation plans. In particular, our relaxation provides a tractable (polynomial-time) algorithm to compute globally optimal transportation plans (in some instances) together with an accompanying proof of global optimality. Our numerical experiments suggest that the proposed relaxation is strong in that it frequently computes the globally optimal solution. Our Python implementation is available at https://github.com/tbng/gwsdp. | https://openreview.net/pdf/27fc72a84eb27e72c7b20792e2bcf5a42dfcc79a.pdf |
TableRAG: Million-Token Table Understanding with Language Models | https://openreview.net/forum?id=41lovPOCo5 | https://openreview.net/forum?id=41lovPOCo5 | Si-An Chen,Lesly Miculicich,Julian Martin Eisenschlos,Zifeng Wang,Zilong Wang,Yanfei Chen,Yasuhisa Fujii,Hsuan-Tien Lin,Chen-Yu Lee,Tomas Pfister | NIPS 2024,Poster | Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables.
However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints.
In response to these challenges, we introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding.
TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs.
This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss.
We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.
Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding. | https://openreview.net/pdf/500503c127a0798de73d7b290c7c0f3280df87b7.pdf |
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning | https://openreview.net/forum?id=OPrPegYIZo | https://openreview.net/forum?id=OPrPegYIZo | Anthony Liang,Guy Tennenholtz,ChihWei Hsu,Yinlam Chow,Erdem Biyik,Craig Boutilier | NIPS 2024,Poster | We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions---parts of the episode where the latent state is fixed---and propose three key modifications to existing meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) prior latent conditioning. We demonstrate the importance of these modifications in various domains, ranging from discrete Gridworld environments to continuous-control and simulated robot assistive tasks, illustrating the efficacy of DynaMITE-RL over state-of-the-art baselines in both online and offline RL settings. | https://openreview.net/pdf/816ee5b0296158f0938d78ab4b84abe989d5fbfc.pdf |
Efficient Temporal Action Segmentation via Boundary-aware Query Voting | https://openreview.net/forum?id=jij4vOVU7i | https://openreview.net/forum?id=jij4vOVU7i | Peiyao Wang,Yuewei Lin,Erik Blasch,Jie Wei,Haibin Ling | NIPS 2024,Poster | Although the performance of Temporal Action Segmentation (TAS) has been improved in recent years, achieving promising results often comes with a high computational cost due to dense inputs, complex model structures, and resource-intensive post-processing requirements. To improve the efficiency while keeping the high performance, we present a novel perspective centered on per-segment classification. By harnessing the capabilities of Transformers, we tokenize each video segment as an instance token, endowed with intrinsic instance segmentation. To realize efficient action segmentation, we introduce BaFormer, a boundary-aware Transformer network. It employs instance queries for instance segmentation and a global query for class-agnostic boundary prediction, yielding continuous segment proposals. During inference, BaFormer employs a simple yet effective voting strategy to classify boundary-wise segments based on instance segmentation. Remarkably, as a single-stage approach, BaFormer significantly reduces the computational costs, utilizing only 6% of the running time compared to the state-of-the-art method DiffAct, while producing better or comparable accuracy over several popular benchmarks. The code for this project is publicly available at https://github.com/peiyao-w/BaFormer. | https://openreview.net/pdf/e567b4b19df3f5ef6569ca19f112a8d20782f12f.pdf |
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand | https://openreview.net/forum?id=vymkuBMLlh | https://openreview.net/forum?id=vymkuBMLlh | Md Musfiqur Rahman,Matt Jordan,Murat Kocaoglu | NIPS 2024,Poster | Causal inference from observational data plays critical role in many applications in trustworthy machine learning.
While sound and complete algorithms exist to compute causal effects, many of them assume access to conditional likelihoods,
which is difficult to estimate for high-dimensional (particularly image) data. Researchers have alleviated this issue by simulating causal relations with neural models. However, when we have high-dimensional variables in the causal graph along with some unobserved confounders, no existing work can effectively sample from the un/conditional interventional distributions. In this work, we show how to sample from any identifiable interventional distribution given an arbitrary causal graph through a sequence of push-forward computations of conditional generative models, such as diffusion models. Our proposed algorithm follows the recursive steps of the existing likelihood-based identification algorithms to train a set of feed-forward models, and connect them in a specific way to sample from the desired distribution. We conduct experiments on a Colored MNIST dataset having both the treatment ($X$) and the target variables ($Y$) as images and sample from $P(y|do(x))$. Our algorithm also enables us to conduct a causal analysis to evaluate spurious correlations among input features of generative models pre-trained on the CelebA dataset. Finally, we generate high-dimensional interventional samples from the MIMIC-CXR dataset involving text and image variables. | https://openreview.net/pdf/d7a91169682e7030f7d0115904a50cf697b82461.pdf |
A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation | https://openreview.net/forum?id=cs1HISJkLU | https://openreview.net/forum?id=cs1HISJkLU | Gwanghyun Kim,Alonso Martinez,Yu-Chuan Su,Brendan Jou,Jose Lezama,Agrim Gupta,Lijun Yu,Lu Jiang,Aren Jansen,Jacob C Walker,Krishna Somandepalli | NIPS 2024,Poster | Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training a separate model for each task which is expensive. Here, we propose a novel training approach to effectively learn arbitrary conditional distributions in the audiovisual space. Our key contribution lies in how we parameterize the diffusion timestep in the forward diffusion process. Instead of the standard fixed diffusion timestep, we propose applying variable diffusion timesteps across the temporal dimension and across modalities of the inputs. This formulation offers flexibility to introduce variable noise levels for various portions of the input, hence the term mixture of noise levels. We propose a transformer-based audiovisual latent diffusion model and show that it can be trained in a task-agnostic fashion using our approach to enable a variety of audiovisual generation tasks at inference time. Experiments demonstrate the versatility of our method in tackling cross-modal and multimodal interpolation tasks in the audiovisual space. Notably, our proposed approach surpasses baselines in generating temporally and perceptually consistent samples conditioned on the input. Project page: neurips13025.github.io | https://openreview.net/pdf/5e4f46f6f36d9828240aa3f46a012311ce5787f9.pdf |
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations | https://openreview.net/forum?id=XHWkHFWi3k | https://openreview.net/forum?id=XHWkHFWi3k | Nikil Roashan Selvam,Amil Merchant,Stefano Ermon | NIPS 2024,Poster | In diffusion models, samples are generated through an iterative refinement process, requiring hundreds of sequential model evaluations. Several recent methods have introduced approximations (fewer discretization steps or distillation) to trade off speed at the cost of sample quality. In contrast, we introduce Self-Refining Diffusion Samplers (SRDS) that retain sample quality and can improve latency at the cost of additional parallel compute. We take inspiration from the Parareal algorithm, a popular numerical method for parallel-in-time integration of differential equations. In SRDS, a quick but rough estimate of a sample is first created and then iteratively refined in parallel through Parareal iterations. SRDS is not only guaranteed to accurately solve the ODE and converge to the serial solution but also benefits from parallelization across the diffusion trajectory, enabling batched inference and pipelining. As we demonstrate for pre-trained diffusion models, the early convergence of this refinement procedure drastically reduces the number of steps required to produce a sample, speeding up generation for instance by up to 1.7x on a 25-step StableDiffusion-v2 benchmark and up to 4.3x on longer trajectories. | https://openreview.net/pdf/f3027e409105679c2ff8905ad7351a1f5ec1463a.pdf |
Is Value Learning Really the Main Bottleneck in Offline RL? | https://openreview.net/forum?id=nyp59a31Ju | https://openreview.net/forum?id=nyp59a31Ju | Seohong Park,Kevin Frans,Sergey Levine,Aviral Kumar | NIPS 2024,Poster | While imitation learning requires access to high-quality data, offline reinforcement learning (RL) should, in principle, perform similarly or better with substantially lower data quality by using a value function. However, current results indicate that offline RL often performs worse than imitation learning, and it is often unclear what holds back the performance of offline RL. Motivated by this observation, we aim to understand the bottlenecks in current offline RL algorithms. While poor performance of offline RL is typically attributed to an imperfect value function, we ask: *is the main bottleneck of offline RL indeed in learning the value function, or something else?* To answer this question, we perform a systematic empirical study of (1) value learning, (2) policy extraction, and (3) policy generalization in offline RL problems, analyzing how these components affect performance. We make two surprising observations. First, we find that the choice of a policy extraction algorithm significantly affects the performance and scalability of offline RL, often more so than the value learning objective. For instance, we show that common value-weighted behavioral cloning objectives (e.g., AWR) do not fully leverage the learned value function, and switching to behavior-constrained policy gradient objectives (e.g., DDPG+BC) often leads to substantial improvements in performance and scalability. Second, we find that a big barrier to improving offline RL performance is often imperfect policy generalization on test-time states out of the support of the training data, rather than policy learning on in-distribution states. We then show that the use of suboptimal but high-coverage data or test-time policy training techniques can address this generalization issue in practice. Specifically, we propose two simple test-time policy improvement methods and show that these methods lead to better performance. | https://openreview.net/pdf/d91da7edfb55832deba6ddea4345f1b3e29cb1b5.pdf |
Chain of Thoughtlessness? An Analysis of CoT in Planning | https://openreview.net/forum?id=kPBEAZU5Nm | https://openreview.net/forum?id=kPBEAZU5Nm | Kaya Stechly,Karthik Valmeekam,Subbarao Kambhampati | NIPS 2024,Poster | Large language model (LLM) performance on reasoning problems typically does not generalize out of distribution. Previous work has claimed that this can be mitigated with chain of thought prompting--a method of demonstrating solution procedures--with the intuition that it is possible to in-context teach an LLM an algorithm for solving the problem.
This paper presents a case study of chain of thought on problems from Blocksworld, a classical planning domain, and examines the performance of two state-of-the-art LLMs across two axes: generality of examples given in prompt, and complexity of problems queried with each prompt. While our problems are very simple, we only find meaningful performance improvements from chain of thought prompts when those prompts are exceedingly specific to their problem class, and that those improvements quickly deteriorate as the size n of the query-specified stack grows past the size of stacks shown in the examples.
We also create scalable variants of three domains commonly studied in previous CoT papers and demonstrate the existence of similar failure modes.
Our results hint that, contrary to previous claims in the literature, CoT's performance improvements do not stem from the model learning general algorithmic procedures via demonstrations but depend on carefully engineering highly problem specific prompts. This spotlights drawbacks of chain of thought, especially the sharp tradeoff between possible performance gains and the amount of human labor necessary to generate examples with correct reasoning traces. | https://openreview.net/pdf/571aa30896a391c557d296759a7f6b04f53b9ed2.pdf |
Understanding the Gains from Repeated Self-Distillation | https://openreview.net/forum?id=gMqaKJCOCB | https://openreview.net/forum?id=gMqaKJCOCB | Divyansh Pareek,Simon Shaolei Du,Sewoong Oh | NIPS 2024,Poster | Self-Distillation is a special type of knowledge distillation where the student model has the same architecture as the teacher model. Despite using the same architecture and the same training data, self-distillation has been empirically observed to improve performance, especially when applied repeatedly. For such a process, there is a fundamental question of interest: How much gain is possible by applying multiple steps of self-distillation? To investigate this relative gain, we propose using the simple but canonical task of linear regression. Our analysis shows that the excess risk achieved by multi-step self-distillation can significantly improve upon a single step of self-distillation, reducing the excess risk by a factor of $d$, where $d$ is the input dimension. Empirical results on regression tasks from the UCI repository show a reduction in the learnt model's risk (MSE) by up to $47$%. | https://openreview.net/pdf/d7ff32ece89462affe997f6189eed65267fa5bc3.pdf |
Recursive Introspection: Teaching Language Model Agents How to Self-Improve | https://openreview.net/forum?id=DRC9pZwBwR | https://openreview.net/forum?id=DRC9pZwBwR | Yuxiao Qu,Tianjun Zhang,Naman Garg,Aviral Kumar | NIPS 2024,Poster | A central piece in enabling intelligent agentic behavior in foundation models is to make them capable of introspecting upon their behavior, reasoning, and correcting their mistakes as more computation or interaction is available. Even the strongest proprietary large language models (LLMs) do not quite exhibit the ability of continually improving their responses sequentially. In this paper, we develop $\textbf{RISE:}$ $\textbf{R}$ecursive $\textbf{I}$ntro$\textbf{S}$p$\textbf{E}$ction, an approach for fine-tuning LLMs to introduce this capability, despite prior work hypothesizing that this capability may not be possible to attain. Our approach prescribes an iterative fine-tuning procedure, which attempts to teach the model how to alter its response after having executed previously unsuccessful attempts to solve a hard test-time problem, with optionally additional environment feedback. RISE poses fine-tuning for a single-turn prompt as solving a multi-turn Markov decision process (MDP), where the initial state is the prompt. Inspired by principles in online imitation and offline reinforcement learning, we propose strategies for multi-turn data collection and training so as to imbue an LLM with the capability to recursively detect and correct its previous mistakes in subsequent iterations. Our experiments show that RISE enables Llama2, Llama3, and Mistral models to improve themselves with more turns on reasoning tasks, outperforming several single-turn strategies given an equal amount of inference-time computation. We also find that RISE scales well, often attaining larger benefits with more capable models, without disrupting one-turn abilities as a result of expressing more complex distributions. | https://openreview.net/pdf/f50ad94a939b176eb3bdf712e863034fc3076193.pdf |
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification | https://openreview.net/forum?id=wqs2RMq4CW | https://openreview.net/forum?id=wqs2RMq4CW | Haolin Liu,Artin Tajdini,Andrew Wagenmaker,Chen-Yu Wei | NIPS 2024,Poster | In linear bandits, how can a learner effectively learn when facing corrupted rewards? While significant work has explored this question, a holistic understanding across different adversarial models and corruption measures is lacking, as is a full characterization of the minimax regret bounds. In this work, we compare two types of corruptions commonly considered: strong corruption, where the corruption level depends on the learner’s chosen action, and weak corruption, where the corruption level does not depend on the learner’s chosen action. We provide a unified framework to analyze these corruptions. For stochastic linear bandits, we fully characterize the gap between the minimax regret under strong and weak corruptions. We also initiate the study of corrupted adversarial linear bandits, obtaining upper and lower bounds with matching dependencies on the corruption level. Next, we reveal a connection between corruption-robust learning and learning with gap-dependent misspecification—a setting first studied by Liu et al. (2023a), where the misspecification level of an action or policy is proportional to its suboptimality. We present a general reduction that enables any corruption-robust algorithm to handle gap-dependent misspecification. This allows us to recover the results of Liu et al. (2023a) in a black-box manner and significantly generalize them to settings like linear MDPs, yielding the first results for gap-dependent misspecification in reinforcement learning. However, this general reduction does not attain the optimal rate for gap-dependent misspecification. Motivated by this, we develop a specialized algorithm that achieves optimal bounds for gap-dependent misspecification in linear bandits, thus answering an open question posed by Liu et al. (2023a). | https://openreview.net/pdf/d72fd0fc0d62a924ff97b58e851197a74e7f045b.pdf |
Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line | https://openreview.net/forum?id=giXUx4VH9t | https://openreview.net/forum?id=giXUx4VH9t | Eungyeup Kim,Mingjie Sun,Christina Baek,Aditi Raghunathan,J Zico Kolter | NIPS 2024,Poster | Recently, Miller et al. (2021) and Baek et al. (2022) empirically demonstrated strong linear correlations between in-distribution (ID) versus out-of-distribution (OOD) accuracy and agreement. These trends, coined accuracy-on-the-line (ACL) and agreement-on-the-line (AGL), enable OOD model selection and performance estimation without labeled data. However, these phenomena also break for certain shifts, such as CIFAR10-C Gaussian Noise, posing a critical bottleneck. In this paper, we make a key finding that recent test-time adaptation (TTA) methods not only improve OOD performance, but it drastically strengthen the ACL and AGL trends in models, even in shifts where models showed very weak correlations before. To analyze this, we revisit the theoretical conditions from Miller et al. (2021) that outline the types of distribution shifts needed for perfect ACL in linear models. Surprisingly, these conditions are satisfied after applying TTA to deep models in the penultimate feature embedding space. In particular, TTA causes the data distribution to collapse complex shifts into those can be expressed by a singular "scaling" variable in the feature space. Our results show that by combining TTA with AGL-based estimation methods, we can estimate the OOD performance of models with high precision for a broader set of distribution shifts. This lends us a simple system for selecting the best hyperparameters and adaptation strategy without any OOD labeled data. Code is available at https://github.com/EungyeupKim/TTALine. | https://openreview.net/pdf/d3bf71ef39b171b5e14b67edd0789e1445dd32c0.pdf |
LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate | https://openreview.net/forum?id=o7DOGbZeyP | https://openreview.net/forum?id=o7DOGbZeyP | Anthony Fuller,Daniel Kyrollos,Yousef Yassin,James R Green | NIPS 2024,Poster | High-resolution images offer more information about scenes that can improve model accuracy. However, the dominant model architecture in computer vision, the vision transformer (ViT), cannot effectively leverage larger images without finetuning — ViTs poorly extrapolate to more patches at test time, although transformers offer sequence length flexibility. We attribute this shortcoming to the current patch position encoding methods, which create a distribution shift when extrapolating.
We propose a drop-in replacement for the position encoding of plain ViTs that restricts attention heads to fixed fields of view, pointed in different directions, using 2D attention masks. Our novel method, called LookHere, provides translation-equivariance, ensures attention head diversity, and limits the distribution shift that attention heads face when extrapolating. We demonstrate that LookHere improves performance on classification (avg. 1.6%), against adversarial attack (avg. 5.4%), and decreases calibration error (avg. 1.5%) — on ImageNet without extrapolation. With extrapolation, LookHere outperforms the current SoTA position encoding method, 2D-RoPE, by 21.7% on ImageNet when trained at $224^2$ px and tested at $1024^2$ px. Additionally, we release a high-resolution test set to improve the evaluation of high-resolution image classifiers, called ImageNet-HR. | https://openreview.net/pdf/c456ddc1b0dbd841b80ff6464ae6d63c046b2815.pdf |
CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions | https://openreview.net/forum?id=mpDbWjLzfT | https://openreview.net/forum?id=mpDbWjLzfT | Sk Miraj Ahmed,Fahim Faisal Niloy,Xiangyu Chang,Dripta S. Raychaudhuri,Samet Oymak,Amit Roy-Chowdhury | NIPS 2024,Poster | Adapting to dynamic data distributions is a practical yet challenging task. One effective strategy is to use a model ensemble, which leverages the diverse expertise of different models to transfer knowledge to evolving data distributions. However, this approach faces difficulties when the dynamic test distribution is available only in small batches and without access to the original source data. To address the challenge of adapting to dynamic distributions in such practical settings, we propose continual multi-source adaptation to dynamic distributions (CONTRAST), a novel method that optimally combines multiple source models to adapt to the dynamic test data. CONTRAST has two distinguishing features. First, it efficiently computes the optimal combination weights to combine the source models to adapt to the test data distribution continuously as a function of time. Second, it identifies which of the source model parameters to update so that only the model which is most correlated to the target data is adapted, leaving the less correlated ones untouched; this mitigates the issue of ``forgetting" the source model parameters by focusing only on the source model that exhibits the strongest correlation with the test batch distribution. Through theoretical analysis we show that the proposed method is able to optimally combine the source models and prioritize updates to the model least prone to forgetting. Experimental analysis on diverse datasets demonstrates that the combination of multiple source models does at least as well as the best source (with hindsight knowledge), and performance does not degrade as the test data distribution changes over time (robust to forgetting). | https://openreview.net/pdf/e782de99a42bd7cbb46ed030b5bdbf45b070ae1f.pdf |
A Simple yet Universal Framework for Depth Completion | https://openreview.net/forum?id=Y4tHp5Jilp | https://openreview.net/forum?id=Y4tHp5Jilp | Jin-Hwi Park,Hae-Gon Jeon | NIPS 2024,Poster | Consistent depth estimation across diverse scenes and sensors is a crucial challenge in computer vision, especially when deploying machine learning models in the real world. Traditional methods depend heavily on extensive pixel-wise labeled data, which is costly and labor-intensive to acquire, and frequently have difficulty in scale issues on various depth sensors. In response, we define Universal Depth Completion (UniDC) problem. We also present a baseline architecture, a simple yet effective approach tailored to estimate scene depth across a wide range of sensors and environments using minimal labeled data.
Our approach addresses two primary challenges: generalizable knowledge of unseen scene configurations and strong adaptation to arbitrary depth sensors with various specifications. To enhance versatility in the wild, we utilize a foundation model for monocular depth estimation that provides a comprehensive understanding of 3D structures in scenes. Additionally, for fast adaptation to off-the-shelf sensors, we generate a pixel-wise affinity map based on the knowledge from the foundation model. We then adjust depth information from arbitrary sensors to the monocular depth along with the constructed affinity. Furthermore, to boost up both the adaptability and generality, we embed the learned features into hyperbolic space, which builds implicit hierarchical structures of 3D data from fewer examples. Extensive experiments demonstrate the proposed method's superior generalization capabilities for UniDC problem over state-of-the-art depth completion. Source code is publicly available at https://github.com/JinhwiPark/UniDC. | https://openreview.net/pdf/8105f4aa92bb3f5ae688b7551ee814d2f3bbaa3a.pdf |
Unconditional stability of a recurrent neural circuit implementing divisive normalization | https://openreview.net/forum?id=5lLb7aXRN9 | https://openreview.net/forum?id=5lLb7aXRN9 | Shivang Rawat,David Heeger,Stefano Martiniani | NIPS 2024,Poster | Stability in recurrent neural models poses a significant challenge, particularly in developing biologically plausible neurodynamical models that can be seamlessly trained. Traditional cortical circuit models are notoriously difficult to train due to expansive nonlinearities in the dynamical system, leading to an optimization problem with nonlinear stability constraints that are difficult to impose. Conversely, recurrent neural networks (RNNs) excel in tasks involving sequential data but lack biological plausibility and interpretability. In this work, we address these challenges by linking dynamic divisive normalization (DN) to the stability of "oscillatory recurrent gated neural integrator circuits'' (ORGaNICs), a biologically plausible recurrent cortical circuit model that dynamically achieves DN and that has been shown to simulate a wide range of neurophysiological phenomena. By using the indirect method of Lyapunov, we prove the remarkable property of unconditional local stability for an arbitrary-dimensional ORGaNICs circuit when the recurrent weight matrix is the identity. We thus connect ORGaNICs to a system of coupled damped harmonic oscillators, which enables us to derive the circuit's energy function, providing a normative principle of what the circuit, and individual neurons, aim to accomplish. Further, for a generic recurrent weight matrix, we prove the stability of the 2D model and demonstrate empirically that stability holds in higher dimensions. Finally, we show that ORGaNICs can be trained by backpropagation through time without gradient clipping/scaling, thanks to its intrinsic stability property and adaptive time constants, which address the problems of exploding, vanishing, and oscillating gradients. By evaluating the model's performance on RNN benchmarks, we find that ORGaNICs outperform alternative neurodynamical models on static image classification tasks and perform comparably to LSTMs on sequential tasks. | https://openreview.net/pdf/80cde123e8722b065a22110db6eebbdfbc4a798b.pdf |
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning | https://openreview.net/forum?id=cIVj8xLVZh | https://openreview.net/forum?id=cIVj8xLVZh | Anwesa Choudhuri,Girish Chowdhary,Alex Schwing | NIPS 2024,Poster | We propose the new task open-world video instance segmentation and captioning. It requires to detect, segment, track and describe with rich captions never before seen objects. This challenging task can be addressed by developing "abstractors" which connect a vision model and a language foundation model. Concretely, we connect a multi-scale visual feature extractor and a large language model (LLM) by developing an object abstractor and an object-to-text abstractor. The object abstractor, consisting of a prompt encoder and transformer blocks, introduces spatially-diverse open-world object queries to discover never before seen objects in videos. An inter-query contrastive loss further encourages the diversity of object queries. The object-to-text abstractor is augmented with masked cross-attention and acts as a bridge between the object queries and a frozen LLM to generate rich and descriptive object-centric captions for each detected object. Our generalized approach surpasses the baseline that jointly addresses the tasks of open-world video instance segmentation and dense video object captioning by 13% on never before seen objects, and by 10% on object-centric captions. | https://openreview.net/pdf/047abf75fc1793c3d73e1480cfd9b6ee77d70fc1.pdf |
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL | https://openreview.net/forum?id=JjQl8hXJAS | https://openreview.net/forum?id=JjQl8hXJAS | Andrew Wagenmaker,Kevin Huang,Liyiming Ke,Kevin Jamieson,Abhishek Gupta | NIPS 2024,Poster | In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy this policy in the real world, with the hope that it generalizes effectively. Such \emph{direct sim2real} transfer is not guaranteed to succeed, however, and in cases where it fails, it is unclear how to best utilize the simulator. In this work, we show that in many regimes, while direct sim2real transfer may fail, we can utilize the simulator to learn a set of \emph{exploratory} policies which enable efficient exploration in the real world. In particular, in the setting of low-rank MDPs, we show that coupling these exploratory policies with simple, practical approaches---least-squares regression oracles and naive randomized exploration---yields a polynomial sample complexity in the real world, an exponential improvement over direct sim2real transfer, or learning without access to a simulator. To the best of our knowledge, this is the first evidence that simulation transfer yields a provable gain in reinforcement learning in settings where direct sim2real transfer fails. We validate our theoretical results on several realistic robotic simulators and a real-world robotic sim2real task, demonstrating that transferring exploratory policies can yield substantial gains in practice as well. | https://openreview.net/pdf/e749c63c44906a2b940f81830331ae3d5f02f741.pdf |
Qualitative Mechanism Independence | https://openreview.net/forum?id=RE5LSV8QYH | https://openreview.net/forum?id=RE5LSV8QYH | Oliver Ethan Richardson,Spencer J Peters,Joseph Halpern | NIPS 2024,Poster | We define what it means for a joint probability distribution to be compatible with aset of independent causal mechanisms, at a qualitative level—or, more precisely with a directed hypergraph $\mathcal A$, which is the qualitative structure of a probabilistic dependency graph (PDG). When A represents a qualitative Bayesian network, QIM-compatibility with $\mathcal A$ reduces to satisfying the appropriate conditional independencies. But giving semantics to hypergraphs using QIM-compatibility lets us do much more. For one thing, we can capture functional dependencies. For another, we can capture important aspects of causality using compatibility: we can use compatibility to understand cyclic causal graphs, and to demonstrate structural compatibility, we must essentially produce a causal model. Finally, compatibility has deep connections to information theory. Applying compatibility to cyclic structures helps to clarify a longstanding conceptual issue in information theory. | https://openreview.net/pdf/0f664f7585fb4d89c8626b0f25948abcb1290e85.pdf |
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference | https://openreview.net/forum?id=PoCs4jq7cV | https://openreview.net/forum?id=PoCs4jq7cV | Benjamin Eysenbach,Vivek Myers,Russ Salakhutdinov,Sergey Levine | NIPS 2024,Poster | Given time series data, how can we answer questions like ``what will happen in the future?'' and ``how did we get here?'' These sorts of probabilistic inference questions are challenging when observations are high-dimensional. In this paper, we show how these questions can have compact, closed form solutions in terms of learned representations. The key idea is to apply a variant of contrastive learning to time series data. Prior work already shows that the representations learned by contrastive learning encode a probability ratio. By extending prior work to show that the marginal distribution over representations is Gaussian, we can then prove that joint distribution of representations is also Gaussian. Taken together, these results show that representations learned via temporal contrastive learning follow a Gauss-Markov chain, a graphical model where inference (e.g., prediction, planning) over representations corresponds to inverting a low-dimensional matrix. In one special case, inferring intermediate representations will be equivalent to interpolating between the learned representations. We validate our theory using numerical simulations on tasks up to 46-dimensions. | https://openreview.net/pdf/5c5066a4b5b8b3f0f1c5836336c061c373e5a190.pdf |
Pricing and Competition for Generative AI | https://openreview.net/forum?id=8LbJfEjIrT | https://openreview.net/forum?id=8LbJfEjIrT | Rafid Mahmood | NIPS 2024,Poster | Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given these characteristics, we explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies who sequentially release their models before users choose their preferred model for each task. Here, the price optimization problem becomes piecewise continuous where the companies must choose a subset of the tasks on which to be cost-effective and forgo revenue for the remaining tasks. In particular, we reveal the value of market information by showing that a company who deploys later after knowing their competitor’s price can always secure cost-effectiveness on at least one task, whereas the company who is the first-to-market must price their model in a way that incentivizes higher prices from the latecomer in order to gain revenue. Most importantly, we find that if the different tasks are sufficiently similar, the first-to-market model may become cost-ineffective on all tasks regardless of how this technology is priced. | https://openreview.net/pdf/2a1a36420317e5da9e570ef1b33d018bdaeded34.pdf |
Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents | https://openreview.net/forum?id=Vq2kzpig8v | https://openreview.net/forum?id=Vq2kzpig8v | John Luoyu Zhou,Weizhe Hong,Jonathan Kao | NIPS 2024,Poster | Cooperation between self-interested individuals is a widespread phenomenon in the natural world, but remains elusive in interactions between artificially intelligent agents. Instead, naïve reinforcement learning algorithms typically converge to Pareto-dominated outcomes in even the simplest of social dilemmas. An emerging literature on opponent shaping has demonstrated the ability to reach prosocial outcomes by influencing the learning of other agents. However, such methods differentiate through the learning step of other agents or optimize for meta-game dynamics, which rely on privileged access to opponents' learning algorithms or exponential sample complexity, respectively. To provide a learning rule-agnostic and sample-efficient alternative, we introduce Reciprocators, reinforcement learning agents which are intrinsically motivated to reciprocate the influence of opponents' actions on their returns. This approach seeks to modify other agents' $Q$-values by increasing their return following beneficial actions (with respect to the Reciprocator) and decreasing it after detrimental actions, guiding them towards mutually beneficial actions without directly differentiating through a model of their policy. We show that Reciprocators can be used to promote cooperation in temporally extended social dilemmas during simultaneous learning. Our code is available at https://github.com/johnlyzhou/reciprocator/. | https://openreview.net/pdf/45d35fd1191c62185b2756971c6888092bc3f045.pdf |
Fair Secretaries with Unfair Predictions | https://openreview.net/forum?id=dxxj4S06YL | https://openreview.net/forum?id=dxxj4S06YL | Eric Balkanski,Will Ma,Andreas Maggiori | NIPS 2024,Poster | Algorithms with predictions is a recent framework for decision-making under uncertainty that leverages the power of machine-learned predictions without making any assumption about their quality. The goal in this framework is for algorithms to achieve an improved performance when the predictions are accurate while maintaining acceptable guarantees when the predictions are erroneous. A serious concern with algorithms that use predictions is that these predictions can be biased and, as a result, cause the algorithm to make decisions that are deemed unfair. We show that this concern manifests itself in the classical secretary problem in the learning-augmented setting---the state-of-the-art algorithm can have zero probability of accepting the best candidate, which we deem unfair, despite promising to accept a candidate whose expected value is at least $\max\{\Omega (1) , 1 - O(\varepsilon)\}$ times the optimal value, where $\varepsilon$ is the prediction error.
We show how to preserve this promise while also guaranteeing to accept the best candidate with probability $\Omega(1)$. Our algorithm and analysis are based on a new ``pegging'' idea that diverges from existing works and simplifies/unifies some of their results. Finally, we extend to the $k$-secretary problem and complement our theoretical analysis with experiments. | https://openreview.net/pdf/80c675fc8ed958e7a8de38f6c3bcd921119ed877.pdf |
Fully Unconstrained Online Learning | https://openreview.net/forum?id=BtCrHwiBHP | https://openreview.net/forum?id=BtCrHwiBHP | Ashok Cutkosky,Zakaria Mhammedi | NIPS 2024,Poster | We provide a technique for OLO that obtains regret $G\|w_\star\|\sqrt{T\log(\|w_\star\|G\sqrt{T})} + \|w_\star\|^2 + G^2$ on $G$-Lipschitz losses for any comparison point $w_\star$ without knowing either $G$ or $\|w_\star\|$. Importantly, this matches the optimal bound $G\|w_\star\|\sqrt{T}$ available with such knowledge (up to logarithmic factors), unless either $\|w_\star\|$ or $G$ is so large that even $G\|w_\star\|\sqrt{T}$ is roughly linear in $T$. Thus, at a high level it matches the optimal bound in all cases in which one can achieve sublinear regret. | https://openreview.net/pdf/e8ad1c22b97a575228aaa4ae662d40d1582160af.pdf |
Advection Augmented Convolutional Neural Networks | https://openreview.net/forum?id=jgpWXnXdME | https://openreview.net/forum?id=jgpWXnXdME | Niloufar Zakariaei,Siddharth Rout,Eldad Haber,Moshe Eliasof | NIPS 2024,Poster | Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation and video prediction. Modern techniques for the solution of these problems typically combine Convolution Neural Networks (CNN) architecture with a time prediction mechanism. However, oftentimes, such approaches underperform in the long-range propagation of information and lack explainability. In this work, we introduce a physically inspired architecture for the solution of such problems. Namely, we propose to augment CNNs with advection by designing a novel semi-Lagrangian push operator. We show that the proposed operator allows for the non-local transformation of information compared with standard convolutional kernels. We then complement it with Reaction and Diffusion neural components to form a network that mimics the Reaction-Advection-Diffusion equation, in high dimensions. We demonstrate the effectiveness of our network on a number of spatio-temporal datasets that show their merit. Our code is available at https://github.com/Siddharth-Rout/deepADRnet. | https://openreview.net/pdf/88edfa504e2e58cce52fc7bcaaaba0c3c6613dbc.pdf |
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback | https://openreview.net/forum?id=Vn0FWRImra | https://openreview.net/forum?id=Vn0FWRImra | Artin Tajdini,Lalit K Jain,Kevin Jamieson | NIPS 2024,Poster | We consider maximizing an unknown monotonic, submodular set function $f: 2^{[n]} \rightarrow [0,1]$ with cardinality constraint under stochastic bandit feedback.
At each time $t=1,\dots,T$ the learner chooses a set $S_t \subset [n]$ with $|S_t| \leq k$ and receives reward $f(S_t) + \eta_t$ where $\eta_t$ is mean-zero sub-Gaussian noise.
The objective is to minimize the learner's regret with respect to an approximation of the maximum $f(S_*)$ with $|S_*| = k$, obtained through robust greedy maximization of $f$.
To date, the best regret bound in the literature scales as $k n^{1/3} T^{2/3}$.
And by trivially treating every set as a unique arm one deduces that $\sqrt{ {n \choose k} T }$ is also achievable using standard multi-armed bandit algorithms.
In this work, we establish the first minimax lower bound for this setting that scales like $\tilde{\Omega}(\min_{L \le k}(L^{1/3}n^{1/3}T^{2/3} + \sqrt{{n \choose k - L}T}))$. For a slightly restricted algorithm class, we prove a stronger regret lower bound of $\tilde{\Omega}(\min_{L \le k}(Ln^{1/3}T^{2/3} + \sqrt{{n \choose k - L}T}))$.
Moreover, we propose an algorithm Sub-UCB that achieves regret $\tilde{\mathcal{O}}(\min_{L \le k}(Ln^{1/3}T^{2/3} + \sqrt{{n \choose k - L}T}))$ capable of matching the lower bound on regret for the restricted class up to logarithmic factors. | https://openreview.net/pdf/e074cce8caa93f256b9c2fbbb4c0f4b210013932.pdf |
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers | https://openreview.net/forum?id=WJ04ZX8txM | https://openreview.net/forum?id=WJ04ZX8txM | Yibo Jiang,Goutham Rajendran,Pradeep Kumar Ravikumar,Bryon Aragam | NIPS 2024,Poster | Large Language Models (LLMs) have the capacity to store and recall facts. Through experimentation with open-source models, we observe that this ability to retrieve facts can be easily manipulated by changing contexts, even without altering their factual meanings. These findings highlight that LLMs might behave like an associative memory model where certain tokens in the contexts serve as clues to retrieving facts. We mathematically explore this property by studying how transformers, the building blocks of LLMs, can complete such memory tasks. We study a simple latent concept association problem with a one-layer transformer and we show theoretically and empirically that the transformer gathers information using self-attention and uses the value matrix for associative memory. | https://openreview.net/pdf/b09c3137e86216785baed4f8a020d498082266b8.pdf |
RTify: Aligning Deep Neural Networks with Human Behavioral Decisions | https://openreview.net/forum?id=nTJeOXlWyV | https://openreview.net/forum?id=nTJeOXlWyV | Yu-Ang Cheng,Ivan F Rodriguez Rodriguez,Sixuan Chen,Kohitij Kar,Takeo Watanabe,Thomas Serre | NIPS 2024,Poster | Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs. The approach is extensively evaluated against various psychophysics experiments. We also show that the approximation can be used to optimize an ``ideal-observer'' RNN model to achieve an optimal tradeoff between speed and accuracy without human data. The resulting model is found to account well for human RT data. Finally, we use the approximation to train a deep learning implementation of the popular Wong-Wang decision-making model. The model is integrated with a convolutional neural network (CNN) model of visual processing and evaluated using both artificial and natural image stimuli. Overall, we present a novel framework that helps align current vision models with human behavior, bringing us closer to an integrated model of human vision. | https://openreview.net/pdf/6db9515e0b18b080a2e152c67101dadf6a756c37.pdf |
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time | https://openreview.net/forum?id=KkYZmepjHn | https://openreview.net/forum?id=KkYZmepjHn | Zixiang Chen,Huizhuo Yuan,Yongqian Li,Yiwen Kou,Junkai Zhang,Quanquan Gu | NIPS 2024,Poster | Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under-explored. In this paper, we propose discrete non-Markov diffusion models (DNDM), which naturally induce the predetermined transition time set. This enables a training-free sampling algorithm that significantly reduces the number of function evaluations (i.e., calls to the neural network), making the sampling process much faster. Furthermore, we study the transition from finite to infinite step sampling, offering new insights into bridging the gap between discrete and continuous-time processes for discrete diffusion models. Extensive experiments on natural language generation and machine translation tasks demonstrate the superior performance of our method in terms of both generation speed and sample quality compared to existing methods for discrete diffusion models. Codes are available at \url{https://github.com/uclaml/DNDM}. | https://openreview.net/pdf/cd03a087df1388277628e04341f404d254d386b1.pdf |
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning | https://openreview.net/forum?id=6KDZHgrDhG | https://openreview.net/forum?id=6KDZHgrDhG | Beyazit Yalcinkaya,Niklas Lauffer,Marcell Vazquez-Chanlatte,Sanjit A. Seshia | NIPS 2024,Poster | Goal-conditioned reinforcement learning is a powerful way to control an AI agent's behavior at runtime. That said, popular goal representations, e.g., target states or natural language, are either limited to Markovian tasks or rely on ambiguous task semantics. We propose representing temporal goals using compositions of deterministic finite automata (cDFAs) and use cDFAs to guide RL agents. cDFAs balance the need for formal temporal semantics with ease of interpretation: if one can understand a flow chart, one can understand a cDFA. On the other hand, cDFAs form a countably infinite concept class with Boolean semantics, and subtle changes to the automaton can result in very different tasks, making them difficult to condition agent behavior on. To address this, we observe that all paths through a DFA correspond to a series of reach-avoid tasks and propose pre-training graph neural network embeddings on "reach-avoid derived" DFAs. Through empirical evaluation, we demonstrate that the proposed pre-training method enables zero-shot generalization to various cDFA task classes and accelerated policy specialization without the myopic suboptimality of hierarchical methods. | https://openreview.net/pdf/c75c71b8dd1c4d79af19b4c0988ac2b32d3a451c.pdf |
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains | https://openreview.net/forum?id=Y4mBaZu4vy | https://openreview.net/forum?id=Y4mBaZu4vy | Eric Qu,Aditi S. Krishnapriyan | NIPS 2024,Poster | Scaling has been a critical factor in improving model performance and generalization across various fields of machine learning.
It involves how a model’s performance changes with increases in model size or input data, as well as how efficiently computational resources are utilized to support this growth.
Despite successes in scaling other types of machine learning models, the study of scaling in Neural Network Interatomic Potentials (NNIPs) remains limited. NNIPs act as surrogate models for ab initio quantum mechanical calculations, predicting the energy and forces between atoms in molecules and materials based on atomic configurations. The dominant paradigm in this field is to incorporate numerous physical domain constraints into the model, such as symmetry constraints like rotational equivariance. We contend that these increasingly complex domain constraints inhibit the scaling ability of NNIPs, and such strategies are likely to cause model performance to plateau in the long run. In this work, we take an alternative approach and start by systematically studying NNIP scaling properties and strategies. Our findings indicate that scaling the model through attention mechanisms is both efficient and improves model expressivity. These insights motivate us to develop an NNIP architecture designed for scalability: the Efficiently Scaled Attention Interatomic Potential (EScAIP).
EScAIP leverages a novel multi-head self-attention formulation within graph neural networks, applying attention at the neighbor-level representations.
Implemented with highly-optimized attention GPU kernels, EScAIP achieves substantial gains in efficiency---at least 10x speed up in inference time, 5x less in memory usage---compared to existing NNIP models. EScAIP also achieves state-of-the-art performance on a wide range of datasets including catalysts (OC20 and OC22), molecules (SPICE), and materials (MPTrj).
We emphasize that our approach should be thought of as a philosophy rather than a specific model, representing a proof-of-concept towards developing general-purpose NNIPs that achieve better expressivity through scaling, and continue to scale efficiently with increased computational resources and training data. | https://openreview.net/pdf/24a8b4f2e3844760ba7ee75be691b2edc6db6c15.pdf |
Length Optimization in Conformal Prediction | https://openreview.net/forum?id=E4ILjwzdEA | https://openreview.net/forum?id=E4ILjwzdEA | Shayan Kiyani,George J. Pappas,Hamed Hassani | NIPS 2024,Poster | Conditional validity and length efficiency are two crucial aspects of conformal prediction (CP). Conditional validity ensures accurate uncertainty quantification for data subpopulations, while proper length efficiency ensures that the prediction sets remain informative. Despite significant efforts to address each of these issues individually, a principled framework that reconciles these two objectives has been missing in the CP literature. In this paper, we develop Conformal Prediction with Length-Optimization (CPL) - a novel and practical framework that constructs prediction sets with (near-) optimal length while ensuring conditional validity under various classes of covariate shifts, including the key cases of marginal and group-conditional coverage. In the infinite sample regime, we provide strong duality results which indicate that CPL achieves conditional validity and length optimality. In the finite sample regime, we show that CPL constructs conditionally valid prediction sets. Our extensive empirical evaluations demonstrate the superior prediction set size performance of CPL compared to state-of-the-art methods across diverse real-world and synthetic datasets in classification, regression, and large language model-based multiple choice question answering. An Implementation of our algorithm can be accessed at the following link: https://github.com/shayankiyani98/CP. | https://openreview.net/pdf/5427bae8d8296fbab1a0dd10970cd5980f9baa0b.pdf |
Truthfulness of Calibration Measures | https://openreview.net/forum?id=cDa8hfTyGc | https://openreview.net/forum?id=cDa8hfTyGc | Nika Haghtalab,Mingda Qiao,Kunhe Yang,Eric Zhao | NIPS 2024,Poster | We study calibration measures in a sequential prediction setup. In addition to rewarding accurate predictions (completeness) and penalizing incorrect ones (soundness), an important desideratum of calibration measures is *truthfulness*, a minimal condition for the forecaster not to be incentivized to exploit the system. Formally, a calibration measure is truthful if the forecaster (approximately) minimizes the expected penalty by predicting the conditional expectation of the next outcome, given the prior distribution of outcomes. We conduct a taxonomy of existing calibration measures. Perhaps surprisingly, all of them are far from being truthful. We introduce a new calibration measure termed the *Subsampled Smooth Calibration Error (SSCE)*, which is complete and sound, and under which truthful prediction is optimal up to a constant multiplicative factor. In contrast, under existing calibration measures, there are simple distributions on which a polylogarithmic (or even zero) penalty is achievable, while truthful prediction leads to a polynomial penalty. | https://openreview.net/pdf/7de0f6622f6908feabc59687a846eca21091098c.pdf |
Simplified and Generalized Masked Diffusion for Discrete Data | https://openreview.net/forum?id=xcqSOfHt4g | https://openreview.net/forum?id=xcqSOfHt4g | Jiaxin Shi,Kehang Han,Zhe Wang,Arnaud Doucet,Michalis Titsias | NIPS 2024,Poster | Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data. However, existing work in this area has been hindered by unnecessarily complex model formulations and unclear relationships between different perspectives, leading to suboptimal parameterization, training objectives, and ad hoc adjustments to counteract these issues. In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models. We show that the continuous-time variational objective of masked diffusion models is a simple weighted integral of cross-entropy losses. Our framework also enables training generalized masked diffusion models with state-dependent masking schedules. When evaluated by perplexity, our models trained on OpenWebText surpass prior diffusion language models at GPT-2 scale and demonstrate superior performance on 4 out of 5 zero-shot language modeling tasks. Furthermore, our models vastly outperform previous discrete diffusion models on pixel-level image modeling, achieving 2.75 (CIFAR-10) and 3.40 (ImageNet 64x64) bits per dimension that are better than autoregressive models of similar sizes. | https://openreview.net/pdf/d1e7b0d9ce4ef700190320cf3f0cf3558857545b.pdf |
Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees | https://openreview.net/forum?id=YYnP3Xpv3y | https://openreview.net/forum?id=YYnP3Xpv3y | Sean Jaffe,Alexander Davydov,Deniz Lapsekili,Ambuj Singh,Francesco Bullo | NIPS 2024,Poster | Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the high dimensional LASA, multi-link pendulum, and Rosenbrock datasets. | https://openreview.net/pdf/8cd04bb230dcace06222c3a29aa5e180682aca90.pdf |
A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning | https://openreview.net/forum?id=APBq3KAmFa | https://openreview.net/forum?id=APBq3KAmFa | Julia B Nakhleh,Joseph Shenouda,Robert D Nowak | NIPS 2024,Poster | This paper studies the properties of solutions to multi-task shallow ReLU neural network learning problems, wherein the network is trained to fit a dataset with minimal sum of squared weights. Remarkably, the solutions learned for each individual task resemble those obtained by solving a kernel method, revealing a novel connection between neural networks and kernel methods. It is known that single-task neural network training problems are equivalent to minimum norm interpolation problem in a non-Hilbertian Banach space, and that the solutions of such problems are generally non-unique. In contrast, we prove that the solutions to univariate-input, multi-task neural network interpolation problems are almost always unique, and coincide with the solution to a minimum-norm interpolation problem in a first-order Sobolev (reproducing kernel) Hilbert Space. We also demonstrate a similar phenomenon in the multivariate-input case; specifically, we show that neural network training problems with a large number of diverse tasks are approximately equivalent to an $\ell^2$ (Hilbert space) minimization problem over a fixed kernel determined by the optimal neurons. | https://openreview.net/pdf/4bef968123d583752201a92dcee34b71b3ae85db.pdf |
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration | https://openreview.net/forum?id=T7dS1Ghwwu | https://openreview.net/forum?id=T7dS1Ghwwu | Yuanjie Shi,SUBHANKAR GHOSH,Taha Belkhouja,Jana Doppa,Yan Yan | NIPS 2024,Poster | Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or conditional probability.
Although the valid coverage guarantee has been extensively studied for classification problems, CP often produces large prediction sets which may not be practically useful.
This issue is exacerbated for the setting of class-conditional coverage on imbalanced classification tasks with many and/or imbalanced classes.
This paper proposes the Rank Calibrated Class-conditional CP (RC3P) algorithm to reduce the prediction set sizes to achieve class-conditional coverage, where the valid coverage holds for each class.
In contrast to the standard class-conditional CP (CCP) method that uniformly thresholds the class-wise conformity score for each class, the augmented label rank calibration step allows RC3P to selectively iterate this class-wise thresholding subroutine only for a subset of classes whose class-wise top-$k$ error is small.
We prove that agnostic to the classifier and data distribution, RC3P achieves class-wise coverage. We also show that RC3P reduces the size of prediction sets compared to the CCP method.
Comprehensive experiments on multiple real-world datasets demonstrate that RC3P achieves class-wise coverage and $26.25\\%$ $\downarrow$ reduction in prediction set sizes on average. | https://openreview.net/pdf/7ad03c77e17e962d610bd1999051ca39e4372d0d.pdf |
GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules | https://openreview.net/forum?id=fzlMza6dRZ | https://openreview.net/forum?id=fzlMza6dRZ | Burouj Armgaan,Manthan Dalmia,Sourav Medya,Sayan Ranu | NIPS 2024,Poster | Instance-level explanation of graph neural networks (GNNs) is a well-studied area. These explainers, however, only explain an instance (e.g., a graph) and fail to uncover the combinatorial reasoning learned by a GNN from the training data towards making its predictions. In this work, we introduce GraphTrail, the first end-to-end, global, post-hoc GNN explainer that translates the functioning of a black-box GNN model to a boolean formula over the (sub)graph level concepts without relying on local explainers. GraphTrail is unique in automatically mining the discriminative subgraph-level concepts using Shapley values. Subsequently, the GNN predictions are mapped to a human-interpretable boolean formula over these concepts through symbolic regression. Extensive experiments across diverse datasets and GNN architectures demonstrate significant improvement over existing global explainers in mapping GNN predictions to faithful logical formulae. The robust and accurate performance of GraphTrail makes it invaluable for improving GNNs and facilitates adoption in domains with strict transparency requirements. | https://openreview.net/pdf/476e9d9dcd1fe989b9ed65ef25c804d563cf1340.pdf |
Layer-Adaptive State Pruning for Deep State Space Models | https://openreview.net/forum?id=T9GbbWbNQG | https://openreview.net/forum?id=T9GbbWbNQG | Minseon Gwak,Seongrok Moon,Joohwan Ko,PooGyeon Park | NIPS 2024,Poster | Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computational costs caused by high state dimensions. In this work, we provide a structured pruning method for SSMs, Layer-Adaptive STate pruning (LAST), which reduces the state dimension of each layer in minimizing model-level energy loss by extending modal truncation for a single system. LAST scores are evaluated using $\mathcal{H}_{\infty}$ norms of subsystems for each state and layer-wise energy normalization. The scores serve as global pruning criteria, enabling cross-layer comparison of states and layer-adaptive pruning. Across various sequence benchmarks, LAST optimizes previous SSMs, revealing the redundancy and compressibility of their state spaces. Notably, we demonstrate that, on average, pruning 33\% of states still maintains performance with 0.52\% accuracy loss in multi-input multi-output SSMs without retraining. Code is available at https://github.com/msgwak/LAST. | https://openreview.net/pdf/7550b83912a48326f2dd1fa4a2cd053b02502697.pdf |
Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning | https://openreview.net/forum?id=MXOzgjlWDF | https://openreview.net/forum?id=MXOzgjlWDF | Arijit Sehanobish,Kumar Avinava Dubey,Krzysztof Marcin Choromanski,Somnath Basu Roy Chowdhury,Deepali Jain,Vikas Sindhwani,Snigdha Chaturvedi | NIPS 2024,Poster | Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei at. al 2022). However, fine-tuning these models for downstream tasks is quite expensive due to their large parameter counts. Parameter-efficient fine-tuning (PEFT) approaches have emerged as a viable alternative, allowing us to fine-tune models by updating only a small number of parameters.
In this work, we propose a general framework for parameter efficient fine-tuning (PEFT), based on *structured unrestricted-rank matrices* (SURM) which can serve as a drop-in replacement for popular approaches such as Adapters and LoRA. Unlike other methods like LoRA, SURMs give us more flexibility in finding the right balance between compactness and expressiveness. This is achieved by using *low displacement rank matrices* (LDRMs), which hasn't been used in this context before. SURMs remain competitive with baselines, often providing significant quality improvements while using a smaller parameter budget. SURMs achieve: **5**-**7**% accuracy gains on various image classification tasks while replacing low-rank matrices in LoRA and: up to **12x** reduction of the number of parameters in adapters (with virtually no loss in quality) on the GLUE benchmark. | https://openreview.net/pdf/13d021ceca6e4f8c1d66f8ac57178562c5e644d9.pdf |
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems | https://openreview.net/forum?id=XZ0fpoAKEB | https://openreview.net/forum?id=XZ0fpoAKEB | Maximilian Nickel | NIPS 2024,Poster | Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including positive social impact, nor continue to advance AI research in a scientifically sound way. In this paper, I will show that for widely considered inference settings in complex social systems the train-test paradigm does not only lack a justification but is indeed invalid for any risk estimator, including counterfactual and causal estimators, with high probability. These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large language models, and neither naïve scaling nor limited benchmarks are suited to address this issue. I am illustrating these results via the widely used MovieLens benchmark and conclude by discussing the implications of these results for AI in social systems, including possible remedies such as participatory data curation and open science. | https://openreview.net/pdf/83b67151ce87c2db32db8764ec703c381c935ed5.pdf |
A Simple Framework for Generalization in Visual RL under Dynamic Scene Perturbations | https://openreview.net/forum?id=0AumdfLzpK | https://openreview.net/forum?id=0AumdfLzpK | Wonil Song,Hyesong Choi,Kwanghoon Sohn,Dongbo Min | NIPS 2024,Poster | In the rapidly evolving domain of vision-based deep reinforcement learning (RL), a pivotal challenge is to achieve generalization capability to dynamic environmental changes reflected in visual observations.
Our work delves into the intricacies of this problem, identifying two key issues that appear in previous approaches for visual RL generalization: (i) imbalanced saliency and (ii) observational overfitting.
Imbalanced saliency is a phenomenon where an RL agent disproportionately identifies salient features across consecutive frames in a frame stack.
Observational overfitting occurs when the agent focuses on certain background regions rather than task-relevant objects.
To address these challenges, we present a simple yet effective framework for generalization in visual RL (SimGRL) under dynamic scene perturbations.
First, to mitigate the imbalanced saliency problem, we introduce an architectural modification to the image encoder to stack frames at the feature level rather than the image level.
Simultaneously, to alleviate the observational overfitting problem, we propose a novel technique called shifted random overlay augmentation, which is specifically designed to learn robust representations capable of effectively handling dynamic visual scenes.
Extensive experiments demonstrate the superior generalization capability of SimGRL, achieving state-of-the-art performance in benchmarks including the DeepMind Control Suite. | https://openreview.net/pdf/7435c53f5e4329852f86b28bd17a4fb523861f53.pdf |
Instance-Optimal Private Density Estimation in the Wasserstein Distance | https://openreview.net/forum?id=Apq6corvfZ | https://openreview.net/forum?id=Apq6corvfZ | Vitaly Feldman,Audra McMillan,Satchit Sivakumar,Kunal Talwar | NIPS 2024,Poster | Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances.
For distributions $P$ over $\mathbb{R}$, we consider a strong notion of instance-optimality: an algorithm that uniformly achieves the instance-optimal estimation rate is competitive with an algorithm that is told that the distribution is either $P$ or $Q_P$ for some distribution $Q_P$ whose probability density function (pdf) is within a factor of 2 of the pdf of $P$. For distributions over $\mathbb{R}^2$, we use a slightly different notion of instance optimality. We say that an algorithm is instance-optimal if it is competitive with an algorithm that is given a constant multiplicative approximation of the density of the distribution. We characterize the instance-optimal estimation rates in both these settings and show that they are uniformly achievable (up to polylogarithmic factors). Our approach for $\mathbb{R}^2$ extends to arbitrary metric spaces as it goes via hierarchically separated trees. As a special case our results lead to instance-optimal learning in TV distance for discrete distributions. | https://openreview.net/pdf/f621923d0cce7ced5ddd26bdc3ef54e68c882ef0.pdf |
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph | https://openreview.net/forum?id=5IFeCNA7zR | https://openreview.net/forum?id=5IFeCNA7zR | Zhehao Zhang,Jiaao Chen,Diyi Yang | NIPS 2024,Poster | The current paradigm of evaluating Large Language Models (LLMs) through static benchmarks comes with significant limitations, such as vulnerability to data contamination and a lack of adaptability to the evolving capabilities of LLMs. Therefore, evaluation methods that can adapt and generate evaluation data with controlled complexity are urgently needed. In this work, we introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks. We further use a code-augmented LLM to ensure the label correctness of newly generated data. We apply our DARG framework to diverse reasoning tasks in four domains with 15 state-of-the-art LLMs. Experimental results show that almost all LLMs experience a performance decrease with increased complexity and certain LLMs exhibit significant drops. Additionally, we find that LLMs exhibit more biases when being evaluated via the data generated by DARG with higher complexity levels. These observations provide useful insights into how to dynamically and adaptively evaluate LLMs. | https://openreview.net/pdf/ff0b87684bafe06ad128c8af823ddc1c950244a2.pdf |
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers | https://openreview.net/forum?id=Eok6HbcSRI | https://openreview.net/forum?id=Eok6HbcSRI | Krzysztof Marcin Choromanski,Arijit Sehanobish,Somnath Basu Roy Chowdhury,Han Lin,Kumar Avinava Dubey,Tamas Sarlos,Snigdha Chaturvedi | NIPS 2024,Poster | We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular *low displacement rank*) for integrating tensor fields defined on weighted trees. Several applications of the resulting *fast tree-field integrators* (FTFIs) are presented, including: (a) approximation of graph metrics with tree metrics, (b) graph classification, (c) modeling on meshes, and finally (d) *Topological Transformers* (TTs) (Choromanski et al., 2022) for images. For Topological Transformers, we propose new relative position encoding (RPE) masking mechanisms with as few as **three** extra learnable parameters per Transformer layer, leading to **1.0-1.5\%+** accuracy gains. Importantly, most of FTFIs are **exact** methods, thus numerically equivalent to their brute-force counterparts. When applied to graphs with thousands of nodes, those exact algorithms provide **5.7-13x** speedups. We also provide an extensive theoretical analysis of our methods. | https://openreview.net/pdf/7a7e2f0f3ce5ec564c29c694ce3126506812314f.pdf |
Label Noise: Ignorance Is Bliss | https://openreview.net/forum?id=fTKcqr4xuX | https://openreview.net/forum?id=fTKcqr4xuX | Yilun Zhu,Jianxin Zhang,Aditya Gangrade,Clayton Scott | NIPS 2024,Poster | We establish a new theoretical framework for learning under multi-class, instance-dependent label noise.
This framework casts learning with label
noise as a form of domain adaptation, in particular, domain adaptation
under posterior drift.
We introduce the concept of \emph{relative signal strength} (RSS), a pointwise measure that quantifies the transferability from noisy to clean posterior.
Using RSS, we establish nearly matching upper and lower bounds on the excess risk.
Our theoretical findings support
the simple
\emph{Noise Ignorant Empirical Risk Minimization (NI-ERM)} principle,
which minimizes empirical risk while ignoring label noise.
Finally, we translate this theoretical insight into practice: by
using NI-ERM to fit a linear classifier on top of a self-supervised
feature extractor, we achieve state-of-the-art performance on the
CIFAR-N data challenge. | https://openreview.net/pdf/568741df07c501c0ff4cc330490b22523e8957b3.pdf |
CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference | https://openreview.net/forum?id=1MCseWaFZb | https://openreview.net/forum?id=1MCseWaFZb | Shayan Shekarforoush,David B. Lindell,Marcus A Brubaker,David J. Fleet | NIPS 2024,Poster | Cryo-EM is an increasingly popular method for determining the atomic resolution 3D structure of macromolecular complexes (eg, proteins) from noisy 2D images captured by an electron microscope. The computational task is to reconstruct the 3D density of the particle, along with 3D pose of the particle in each 2D image, for which the posterior pose distribution is highly multi-modal. Recent developments in cryo-EM have focused on deep learning for which amortized inference has been used to predict pose. Here, we address key problems with this approach, and propose a new semi-amortized method, cryoSPIN, in which reconstruction begins with amortized inference and then switches to a form of auto-decoding to refine poses locally using stochastic gradient descent. Through evaluation on synthetic datasets, we demonstrate that cryoSPIN is able to handle multi-modal pose distributions during the amortized inference stage, while the later, more flexible stage of direct pose optimization yields faster and more accurate convergence of poses compared to baselines. On experimental data, we show that cryoSPIN outperforms the state-of-the-art cryoAI in speed and reconstruction quality. | https://openreview.net/pdf/6c4cb8c1896b91037fa7dbe74f5a53bc084258fe.pdf |
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