title
stringlengths 18
162
| url
stringlengths 42
44
| detail_url
stringlengths 42
44
| authors
stringlengths 10
429
| tags
stringclasses 3
values | abstract
stringlengths 400
2.37k
| pdf
stringlengths 71
71
|
|---|---|---|---|---|---|---|
Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency | https://openreview.net/forum?id=8oJHwb3Sgp | https://openreview.net/forum?id=8oJHwb3Sgp | Lingxiao Wang,Qi Cai,Zhuoran Yang,Zhaoran Wang | ICLR 2023,Poster | Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges. (i) It often takes the full history to predict the future, which induces a sample complexity that scales exponentially with the horizon. (ii) The observation and state spaces are often continuous, which induces a sample complexity that scales exponentially with the extrinsic dimension. Addressing such challenges requires learning a minimal but sufficient representation of the observation and state histories by exploiting the structure of the POMDP.
To this end, we propose a reinforcement learning algorithm named Represent to Control (RTC), which learns the representation at two levels while optimizing the policy.~(i) For each step, RTC learns to represent the state with a low-dimensional feature, which factorizes the transition kernel. (ii) Across multiple steps, RTC learns to represent the full history with a low-dimensional embedding, which assembles the per-step feature. We integrate (i) and (ii) in a unified framework that allows a variety of estimators (including maximum likelihood estimators and generative adversarial networks). For a class of POMDPs with a low-rank structure in the transition kernel, RTC attains an $O(1/\epsilon^2)$ sample complexity that scales polynomially with the horizon and the intrinsic dimension (that is, the rank). Here $\epsilon$ is the optimality gap. To our best knowledge, RTC is the first sample-efficient algorithm that bridges representation learning and policy optimization in POMDPs with infinite observation and state spaces. | https://openreview.net/pdf/eaf0d6ae5cbcc76e626aef7d8b1df2b919b74633.pdf |
Towards Better Selective Classification | https://openreview.net/forum?id=5gDz_yTcst | https://openreview.net/forum?id=5gDz_yTcst | Leo Feng,Mohamed Osama Ahmed,Hossein Hajimirsadeghi,Amir H. Abdi | ICLR 2023,Poster | We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset. Recent state-of-the-art selective methods come with architectural changes either via introducing a separate selection head or an extra abstention logit. In this paper, we challenge the aforementioned methods. The results suggest that the superior performance of state-of-the-art methods is owed to training a more generalizable classifier rather than their proposed selection mechanisms. We argue that the best performing selection mechanism should instead be rooted in the classifier itself. Our proposed selection strategy uses the classification scores and achieves better results by a significant margin, consistently, across all coverages and all datasets, without any added compute cost. Furthermore, inspired by semi-supervised learning, we propose an entropy-based regularizer that improves the performance of selective classification methods. Our proposed selection mechanism with the proposed entropy-based regularizer achieves new state-of-the-art results. | https://openreview.net/pdf/1ac777458ac95cf127c4cd30ae0597c5f3ffa7a5.pdf |
Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment | https://openreview.net/forum?id=-G1kjTFsSs | https://openreview.net/forum?id=-G1kjTFsSs | Chuanhao Li,Huazheng Wang,Mengdi Wang,Hongning Wang | ICLR 2023,Poster | Despite the recent advances in communication-efficient distributed bandit learning, most existing solutions are restricted to parametric models, e.g., linear bandits and generalized linear bandits (GLB). In comparison, kernel bandits, which search for non-parametric functions in a reproducing kernel Hilbert space (RKHS), offer higher modeling capacity. But the only existing work in distributed kernel bandits adopts a synchronous communication protocol, which greatly limits its practical use (e.g., every synchronization step requires all clients to participate and wait for data exchange).
In this paper, in order to improve the robustness against delays and unavailability of clients that are common in practice, we propose the first asynchronous solution based on approximated kernel regression for distributed kernel bandit learning. A set of effective treatments are developed to ensure approximation quality and communication efficiency. Rigorous theoretical analysis about the regret and communication cost is provided; and extensive empirical evaluations demonstrate the effectiveness of our solution. | https://openreview.net/pdf/7558858d579b52058b99b1fe6ebb3b9143192941.pdf |
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution | https://openreview.net/forum?id=G_HSyfLk0m | https://openreview.net/forum?id=G_HSyfLk0m | Chao Chen,Haoyu Geng,Gang Zeng,Zhaobing Han,Hua Chai,Xiaokang Yang,Junchi Yan | ICLR 2023,Poster | Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph, then learn structural graph properties to recover the function from its values on certain vertices --- the problem of graph signal sampling. We propose a class of regularization functionals that takes into account discrete random label noise in the graph vertex domain, then develop the GS-IMC approach which biases the reconstruction towards functions that vary little between adjacent vertices for noise reduction. Theoretical result shows that accurate reconstructions can be achieved under mild conditions. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain and builds upon a prediction-correction update algorithm to obtain the unbiased and minimum-variance reconstruction. Both GS-IMC and BGS-IMC have closed-form solutions and thus are highly scalable in large data. Experiments show that our methods achieve state-of-the-art performance on public benchmarks. | https://openreview.net/pdf/6db8d1f31ed96da79d7d225f98a08bd12f58ca1a.pdf |
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers | https://openreview.net/forum?id=cHf1DcCwcH3 | https://openreview.net/forum?id=cHf1DcCwcH3 | Xianbiao Qi,Jianan Wang,Yihao Chen,Yukai Shi,Lei Zhang | ICLR 2023,Poster | We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability. In LipsFormer, we replace unstable Transformer component modules with Lipschitz continuous counterparts: CenterNorm instead of LayerNorm, spectral initialization instead of Xavier initialization, scaled cosine similarity attention instead of dot-product attention, and weighted residual shortcut. We prove that these introduced modules are Lipschitz continuous and derive an upper bound on the Lipschitz constant of LipsFormer. Our experiments show that LipsFormer allows stable training of deep Transformer architectures without the need of careful learning rate tuning such as warmup, yielding a faster convergence and better generalization. As a result, on the ImageNet 1K dataset, LipsFormer-Tiny training for 100 epochs without learning rate warmup attains a top-1 accuracy of 81.6\% which is higher than Swin Transformer-Tiny training for 300 epochs with warmup. Moreover, LipsFormer-Tiny training for 300 epochs achieves a top-1 accuracy of 83.5\% with 4.7G FLOPs and 24M parameters. | https://openreview.net/pdf/439bee65f721160a93f040f8600c3635de5e058a.pdf |
Automatic Chain of Thought Prompting in Large Language Models | https://openreview.net/forum?id=5NTt8GFjUHkr | https://openreview.net/forum?id=5NTt8GFjUHkr | Zhuosheng Zhang,Aston Zhang,Mu Li,Alex Smola | ICLR 2023,Poster | Large Language Models (LLMs) can carry out complex reasoning tasks by generating intermediate reasoning steps. These steps are triggered by what is called chain-of-thought (CoT) prompting, which comes in two flavors: one leverages a simple prompt like "Let’s think step by step" to facilitate step-by-step reasoning before answering a question (Zero-Shot-CoT). The other uses manual demonstrations, each composed of a question and a reasoning chain that leads to an answer (Manual-CoT). Unfortunately, the superior performance of the latter strategy crucially hinges on manually generating task-specific demonstrations. This makes it far less scalable and more dependent on the talent of the CoT engineer. We show that such manual efforts may be eliminated by leveraging LLMs to generate the reasoning chains on its own. Since these generated chains often come with mistakes we propose a number of mitigation strategies. Our proposed Auto-CoT method automaticaly samples diverse questions and we perform post-processing quality control to generate usable reasoning chains from Zero-Shot-CoT. On ten public benchmark reasoning tasks, Auto-CoT performs on par with Manual-CoT without the need for human intervention. Code is available at https://github.com/amazon-research/auto-cot.
| https://openreview.net/pdf/f566e3690bb4730c79fee6cd769ca1c7f2dc2bc6.pdf |
An efficient encoder-decoder architecture with top-down attention for speech separation | https://openreview.net/forum?id=fzberKYWKsI | https://openreview.net/forum?id=fzberKYWKsI | Kai Li,Runxuan Yang,Xiaolin Hu | ICLR 2023,Poster | Deep neural networks have shown excellent prospects in speech separation tasks. However, obtaining good results while keeping a low model complexity remains challenging in real-world applications. In this paper, we provide a bio-inspired efficient encoder-decoder architecture by mimicking the brain’s top-down attention, called TDANet, with decreased model complexity without sacrificing performance. The top-down attention in TDANet is extracted by the global attention (GA) module and the cascaded local attention (LA) layers. The GA module takes multi-scale acoustic features as input to extract global attention signal, which then modulates features of different scales by direct top-down connections. The LA layers use features of adjacent layers as input to extract the local attention signal, which is used to modulate the lateral input in a top-down manner. On three benchmark datasets, TDANet consistently achieved competitive separation performance to previous state-of-the-art (SOTA) methods with higher efficiency. Specifically, TDANet’s multiply-accumulate operations (MACs) are only 5% of Sepformer, one of the previous SOTA models, and CPU inference time is only 10% of Sepformer. In addition, a large-size version of TDANet obtained SOTA results on three datasets, with MACs still only 10% of Sepformer and the CPU inference time only 24% of Sepformer. Our study suggests that top-down attention can be a more efficient strategy for speech separation. | https://openreview.net/pdf/f774281ea7675432c37c07ae84d0162f9a3541e3.pdf |
Machine Unlearning of Federated Clusters | https://openreview.net/forum?id=VzwfoFyYDga | https://openreview.net/forum?id=VzwfoFyYDga | Chao Pan,Jin Sima,Saurav Prakash,Vishal Rana,Olgica Milenkovic | ICLR 2023,Poster | Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has become of significant importance. We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework. Our FC framework utilizes special initialization procedures that we show are well-suited for unlearning. To protect client data privacy, we develop the secure compressed multiset aggregation (SCMA) framework that addresses sparse secure federated learning (FL) problems encountered during clustering as well as more general problems. To simultaneously facilitate low communication complexity and secret sharing protocols, we integrate Reed-Solomon encoding with special evaluation points into our SCMA pipeline, and prove that the client communication cost is logarithmic in the vector dimension. Additionally, to demonstrate the benefits of our unlearning mechanism over complete retraining, we provide a theoretical analysis for the unlearning performance of our approach. Simulation results show that the new FC framework exhibits superior clustering performance compared to previously reported FC baselines when the cluster sizes are highly imbalanced. Compared to completely retraining K-means++ locally and globally for each removal request, our unlearning procedure offers an average speed-up of roughly 84x across seven datasets. Our implementation for the proposed method is available at https://github.com/thupchnsky/mufc. | https://openreview.net/pdf/51ee65b11a32de7ad446a5917d748f9da5399714.pdf |
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning | https://openreview.net/forum?id=7D5EECbOaf9 | https://openreview.net/forum?id=7D5EECbOaf9 | Xiaobo Xia,Jiale Liu,Jun Yu,Xu Shen,Bo Han,Tongliang Liu | ICLR 2023,Poster | Deep learning methods nowadays rely on massive data, resulting in substantial costs of data storage and model training. Data selection is a useful tool to alleviate such costs, where a coreset of massive data is extracted to practically perform on par with full data. Based on carefully-designed score criteria, existing methods first count the score of each data point and then select the data points whose scores lie in a certain range to construct a coreset. These methods work well in their respective preconceived scenarios but are not robust to the change of scenarios, since the optimal range of scores varies as the scenario changes. The issue limits the application of these methods, because realistic scenarios often mismatch preconceived ones, and it is inconvenient or unfeasible to tune the criteria and methods accordingly. In this paper, to address the issue, a concept of the moderate coreset is discussed. Specifically, given any score criterion of data selection, different scenarios prefer data points with scores in different intervals. As the score median is a proxy of the score distribution in statistics, the data points with scores close to the score median can be seen as a proxy of full data and generalize different scenarios, which are used to construct the moderate coreset. As a proof-of-concept, a universal method that inherits the moderate coreset and uses the distance of a data point to its class center as the score criterion, is proposed to meet complex realistic scenarios. Extensive experiments confirm the advance of our method over prior state-of-the-art methods, leading to a strong baseline for future research. The implementation is available at https://github.com/tmllab/Moderate-DS. | https://openreview.net/pdf/a3ac5cd8f08debe7b45e110c1b5a43a6d3615056.pdf |
Federated Nearest Neighbor Machine Translation | https://openreview.net/forum?id=R1U5G2spbLd | https://openreview.net/forum?id=R1U5G2spbLd | Yichao Du,Zhirui Zhang,Bingzhe Wu,Lemao Liu,Tong Xu,Enhong Chen | ICLR 2023,Poster | To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithm (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for machine translation tasks due to the vast communication overheads and heavy synchronization. In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients to build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a $k$-nearest-neighbor ($k$NN) classifier and integrates the external datastore constructed by private text data in all clients to form the final FL model. A two-phase datastore encryption strategy is introduced to achieve privacy-preserving during this process. Extensive experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg, while maintaining promising performance in different FL settings. | https://openreview.net/pdf/d522448b622262195feddfaf6b8265bbd8f42382.pdf |
Latent Variable Representation for Reinforcement Learning | https://openreview.net/forum?id=mQpmZVzXK1h | https://openreview.net/forum?id=mQpmZVzXK1h | Tongzheng Ren,Chenjun Xiao,Tianjun Zhang,Na Li,Zhaoran Wang,sujay sanghavi,Dale Schuurmans,Bo Dai | ICLR 2023,Poster | Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dynamics. On the other hand, it remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of RL. In this paper, we provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle in the face of uncertainty for exploration. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models. Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings. Empirically, we demonstrate superior performance over current state-of-the-art algorithms across various benchmarks. | https://openreview.net/pdf/5c972a086aefc63dad4305fc71633c970805fb48.pdf |
ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs | https://openreview.net/forum?id=2r6YMqz4Mml | https://openreview.net/forum?id=2r6YMqz4Mml | Han Lu,Zenan Li,Runzhong Wang,Qibing Ren,Xijun Li,Mingxuan Yuan,Jia Zeng,Xiaokang Yang,Junchi Yan | ICLR 2023,Poster | Solving combinatorial optimization (CO) on graphs has been attracting increasing interests from the machine learning community whereby data-driven approaches were recently devised to go beyond traditional manually-designated algorithms. In this paper, we study the robustness of a combinatorial solver as a blackbox regardless it is classic or learning-based though the latter can often be more interesting to the ML community. Specifically, we develop a practically feasible robustness metric for general CO solvers. A no-worse optimal cost guarantee is developed as such the optimal solutions are not required to achieve for solvers, and we tackle the non-differentiable challenge in input instance disturbance by resorting to black-box adversarial attack methods. Extensive experiments are conducted on 14 unique combinations of solvers and CO problems, and we demonstrate that the performance of state-of-the-art solvers like Gurobi can degenerate by over 20% under the given time limit bound on the hard instances discovered by our robustness metric, raising concerns about the robustness of combinatorial optimization solvers. | https://openreview.net/pdf/406ee084bb8f0247b906b0b194bf1f0315cb7867.pdf |
Words are all you need? Language as an approximation for human similarity judgments | https://openreview.net/forum?id=O-G91-4cMdv | https://openreview.net/forum?id=O-G91-4cMdv | Raja Marjieh,Pol Van Rijn,Ilia Sucholutsky,Theodore Sumers,Harin Lee,Thomas L. Griffiths,Nori Jacoby | ICLR 2023,Poster | Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model alignment, but classical methods for collecting human similarity judgments are too expensive to be used at scale. Recent methods propose using pre-trained deep neural networks (DNNs) to approximate human similarity, but pre-trained DNNs may not be available for certain domains (e.g., medical images, low-resource languages) and their performance in approximating human similarity has not been extensively tested. We conducted an evaluation of 611 pre-trained models across three domains -- images, audio, video -- and found that there is a large gap in performance between human similarity judgments and pre-trained DNNs. To address this gap, we propose a new class of similarity approximation methods based on language. To collect the language data required by these new methods, we also developed and validated a novel adaptive tag collection pipeline. We find that our proposed language-based methods are significantly cheaper, in the number of human judgments, than classical methods, but still improve performance over the DNN-based methods. Finally, we also develop `stacked' methods that combine language embeddings with DNN embeddings, and find that these consistently provide the best approximations for human similarity across all three of our modalities. Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data. To accompany this guide, we also release all of the similarity and language data, a total of 206,339 human judgments, that we collected in our experiments, along with a detailed breakdown of all modeling results. | https://openreview.net/pdf/e9825a87bbccf79c871d0794f711d687ec5acb48.pdf |
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning | https://openreview.net/forum?id=PDrUPTXJI_A | https://openreview.net/forum?id=PDrUPTXJI_A | Yidong Wang,Hao Chen,Qiang Heng,Wenxin Hou,Yue Fan,Zhen Wu,Jindong Wang,Marios Savvides,Takahiro Shinozaki,Bhiksha Raj,Bernt Schiele,Xing Xie | ICLR 2023,Poster | Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization. However, we argue that existing methods might fail to utilize the unlabeled data more effectively since they either use a pre-defined / fixed threshold or an ad-hoc threshold adjusting scheme, resulting in inferior performance and slow convergence. We first analyze a motivating example to obtain intuitions on the relationship between the desirable threshold and model's learning status. Based on the analysis, we hence propose FreeMatch to adjust the confidence threshold in a self-adaptive manner according to the model's learning status. We further introduce a self-adaptive class fairness regularization penalty to encourage the model for diverse predictions during the early training stage. Extensive experiments indicate the superiority of FreeMatch especially when the labeled data are extremely rare. FreeMatch achieves 5.78%, 13.59%, and 1.28% error rate reduction over the latest state-of-the-art method FlexMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels per class, respectively. Moreover, FreeMatch can also boost the performance of imbalanced SSL. The codes can be found at https://github.com/microsoft/Semi-supervised-learning.
| https://openreview.net/pdf/759017e171fccb1d81f8a521cd4babf7b0d0c16b.pdf |
Confidence Estimation Using Unlabeled Data | https://openreview.net/forum?id=sOXU-PEJSgQ | https://openreview.net/forum?id=sOXU-PEJSgQ | Chen Li,Xiaoling Hu,Chao Chen | ICLR 2023,Poster | Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task. | https://openreview.net/pdf/bc30667c89961e244c8b6d8dd71c2e9dd4e9accf.pdf |
Spectral Decomposition Representation for Reinforcement Learning | https://openreview.net/forum?id=FBMLeaXpZN | https://openreview.net/forum?id=FBMLeaXpZN | Tongzheng Ren,Tianjun Zhang,Lisa Lee,Joseph E. Gonzalez,Dale Schuurmans,Bo Dai | ICLR 2023,Poster | Representation learning often plays a critical role in avoiding the curse of dimensionality in reinforcement learning. A representative class of algorithms exploits spectral decomposition of the stochastic transition dynamics to construct representations that enjoy strong theoretical properties in idealized settings. However, current spectral methods suffer from limited applicability because they are constructed for
state-only aggregation and are derived from a policy-dependent transition kernel, without considering the issue of exploration. To address these issues, we propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy, while also balancing the exploration-versus-exploitation trade-off during learning. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. In addition, an experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several RL benchmarks. | https://openreview.net/pdf/bad3599bfddaf9f576a9c84fc0e1ddb678a2c5dc.pdf |
On Accelerated Perceptrons and Beyond | https://openreview.net/forum?id=fYzLpCsGZVf | https://openreview.net/forum?id=fYzLpCsGZVf | Guanghui Wang,Rafael Hanashiro,Etash Kumar Guha,Jacob Abernethy | ICLR 2023,Poster | The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify $n$ linearly separable data points, assuming the classes are separated by some margin $\gamma > 0$. A foundational result is that Perceptron converges after $\Omega(1/\gamma^{2})$ iterations. There have been several recent works that managed to improve this rate by a quadratic factor, to $\Omega(\sqrt{\log n}/\gamma)$, with more sophisticated algorithms. In this paper, we unify these existing results under one framework by showing that they can all be described through the lens of solving min-max problems using modern acceleration techniques, mainly through \emph{optimistic} online learning. We then show that the proposed framework also leads to improved results for a series of problems beyond the standard Perceptron setting. Specifically, a) for the margin maximization problem, we improve the state-of-the-art result from $O(\log t/t^2)$ to $O(1/t^2)$, where $t$ is the number of iterations; b) we provide the first result on identifying the implicit bias property of the classical Nesterov's accelerated gradient descent (NAG) algorithm, and show NAG can maximize the margin with an $O(1/t^2)$ rate; c) for the classical $p$-norm Perceptron problem, we provide an algorithm with $\Omega(\sqrt{(p-1)\log n}/\gamma)$ convergence rate, while existing algorithms suffer the $\Omega({(p-1)}/\gamma^2)$ convergence rate. | https://openreview.net/pdf/768d155c1f9dc5850d4f0168d19575e8c747490e.pdf |
SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning | https://openreview.net/forum?id=ymt1zQXBDiF | https://openreview.net/forum?id=ymt1zQXBDiF | Hao Chen,Ran Tao,Yue Fan,Yidong Wang,Jindong Wang,Bernt Schiele,Xing Xie,Bhiksha Raj,Marios Savvides | ICLR 2023,Poster | The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrate the inherent quantity-quality trade-off problem of pseudo-labeling with thresholding, which may prohibit learning. To this end, we propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training, effectively exploiting the unlabeled data. We derive a truncated Gaussian function to weight samples based on their confidence, which can be viewed as a soft version of the confidence threshold. We further enhance the utilization of weakly-learned classes by proposing a uniform alignment approach. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification. | https://openreview.net/pdf/2d1c30475b7e47aaec4402b7ffd940072d10b35d.pdf |
Certifiably Robust Policy Learning against Adversarial Multi-Agent Communication | https://openreview.net/forum?id=dCOL0inGl3e | https://openreview.net/forum?id=dCOL0inGl3e | Yanchao Sun,Ruijie Zheng,Parisa Hassanzadeh,Yongyuan Liang,Soheil Feizi,Sumitra Ganesh,Furong Huang | ICLR 2023,Poster | Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored. Specifically, if communication messages are manipulated by malicious attackers, agents relying on untrustworthy communication may take unsafe actions that lead to catastrophic consequences. Therefore, it is crucial to ensure that agents will not be misled by corrupted communication, while still benefiting from benign communication. In this work, we consider an environment with $N$ agents, where the attacker may arbitrarily change the communication from any $C<\frac{N-1}{2}$ agents to a victim agent. For this strong threat model, we propose a certifiable defense by constructing a message-ensemble policy that aggregates multiple randomly ablated message sets. Theoretical analysis shows that this message-ensemble policy can utilize benign communication while being certifiably robust to adversarial communication, regardless of the attacking algorithm. Experiments in multiple environments verify that our defense significantly improves the robustness of trained policies against various types of attacks. | https://openreview.net/pdf/f1c6ea43513dada0ace7e97e3a9c8f26b83250a8.pdf |
Disentangling the Mechanisms Behind Implicit Regularization in SGD | https://openreview.net/forum?id=LE5LxBgjB4V | https://openreview.net/forum?id=LE5LxBgjB4V | Zachary Novack,Simran Kaur,Tanya Marwah,Saurabh Garg,Zachary Chase Lipton | ICLR 2023,Poster | A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD) leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training. However, to date, empirical evidence assessing the explanatory power of these hypotheses is lacking. In this paper, we conduct an extensive empirical evaluation, focusing on the ability of various theorized mechanisms to close the small-to-large batch generalization gap. Additionally, we characterize how the quantities that SGD has been claimed to (implicitly) regularize change over the course of training. By using micro-batches, i.e. disjoint smaller subsets of each mini-batch, we empirically show that explicitly penalizing the gradient norm or the Fisher Information Matrix trace, averaged over micro-batches, in the large-batch regime recovers small-batch SGD generalization, whereas Jacobian-based regularizations fail to do so. This generalization performance is shown to often be correlated with how well the regularized model’s gradient norms resemble those of small-batch SGD. We additionally show that this behavior breaks down as the micro-batch size approaches the batch size. Finally, we note that in this line of inquiry, positive experimental findings on CIFAR10 are often reversed on other datasets like CIFAR100, highlighting the need to test hypotheses on a wider collection of datasets. | https://openreview.net/pdf/c35dec93c98d1cbcaa6f5b89ee16b1bf20203b19.pdf |
Sequential Attention for Feature Selection | https://openreview.net/forum?id=TTLLGx3eet | https://openreview.net/forum?id=TTLLGx3eet | Taisuke Yasuda,Mohammadhossein Bateni,Lin Chen,Matthew Fahrbach,Gang Fu,Vahab Mirrokni | ICLR 2023,Poster | Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on $\ell_1$ regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features during selection, i.e., the marginal contribution of a feature given that other features have already been selected. We propose a feature selection algorithm called Sequential Attention that achieves state-of-the-art empirical results for neural networks. This algorithm is based on an efficient one-pass implementation of greedy forward selection and uses attention weights at each step as a proxy for feature importance. We give theoretical insights into our algorithm for linear regression by showing that an adaptation to this setting is equivalent to the classical Orthogonal Matching Pursuit (OMP) algorithm, and thus inherits all of its provable guarantees. Our theoretical and empirical analyses offer new explanations towards the effectiveness of attention and its connections to overparameterization, which may be of independent interest. | https://openreview.net/pdf/e9214f5e01bc6ffe4029aea4bcdf0d18b1e870cb.pdf |
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs | https://openreview.net/forum?id=jpsw-KuOi7r | https://openreview.net/forum?id=jpsw-KuOi7r | Yuan Cheng,Ruiquan Huang,Yingbin Liang,Jing Yang | ICLR 2023,Poster | In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation and linear weight vectors are unknown. Although various algorithms have been proposed for reward-free low-rank MDPs, the corresponding sample complexity is still far from being satisfactory. In this work, we first provide the first known sample complexity lower bound that holds for any algorithm under low-rank MDPs. This lower bound implies it is strictly harder to find a near-optimal policy under low-rank MDPs than under linear MDPs. We then propose a novel model-based algorithm, coined RAFFLE, and show it can both find an $\epsilon$-optimal policy and achieve an $\epsilon$-accurate system identification via reward-free exploration, with a sample complexity significantly improving the previous results. Such a sample complexity matches our lower bound in the dependence on $\epsilon$, as well as on $K$ {in the large $d$ regime}, where $d$ and $K$ respectively denote the representation dimension and action space cardinality. Finally, we provide a planning algorithm (without further interaction with true environment) for RAFFLE to learn a near-accurate representation, which is the first known representation learning guarantee under the same setting.
| https://openreview.net/pdf/7e35ca55c657bd4a6cd5f8b5855b554ea5a1b927.pdf |
Re-Imagen: Retrieval-Augmented Text-to-Image Generator | https://openreview.net/forum?id=XSEBx0iSjFQ | https://openreview.net/forum?id=XSEBx0iSjFQ | Wenhu Chen,Hexiang Hu,Chitwan Saharia,William W. Cohen | ICLR 2023,Poster | Research on text-to-image generation has witnessed significant progress in generating diverse and photo-realistic images, driven by diffusion and auto-regressive models trained on large-scale image-text data. Though state-of-the-art models can generate high-quality images of common entities, they often have difficulty generating images of uncommon entities, such as `Chortai (dog)' or `Picarones (food)'. To tackle this issue, we present the Retrieval-Augmented Text-to-Image Generator (Re-Imagen), a generative model that uses retrieved information to produce high-fidelity and faithful images, even for rare or unseen entities. Given a text prompt, Re-Imagen accesses an external multi-modal knowledge base to retrieve relevant (image, text) pairs, and uses them as references to generate the image. With this retrieval step, Re-Imagen is augmented with the knowledge of high-level semantics and low-level visual details of the mentioned entities, and thus improves its accuracy in generating the entities' visual appearances. We train Re-Imagen on a constructed dataset containing (image,text,retrieval) triples to teach the model to ground on both text prompt and retrieval. Furthermore, we develop a new sampling strategy to interleave the classifier-free guidance for text and retrieval condition to balance the text and retrieval alignment. Re-Imagen achieves new SoTA FID results on two image generation benchmarks, such as COCO (\ie, FID = 5.25) and WikiImage (\ie, FID = 5.82) without fine-tuning. To further evaluate the capabilities of the model, we introduce EntityDrawBench, a new benchmark that evaluates image generation for diverse entities, from frequent to rare, across multiple visual domains. Human evaluation on EntityDrawBench shows that Re-Imagen performs on par with the best prior models in photo-realism, but with significantly better real-world faithfulness, especially on less frequent entities. | https://openreview.net/pdf/cd7624d9396c506925128bb17ad6c851714db6e4.pdf |
Provably Efficient Lifelong Reinforcement Learning with Linear Representation | https://openreview.net/forum?id=Qd0p0bl-A9t | https://openreview.net/forum?id=Qd0p0bl-A9t | Sanae Amani,Lin Yang,Ching-An Cheng | ICLR 2023,Poster | We theoretically study lifelong reinforcement learning (RL) with linear representation in a regret minimization setting. The goal of the agent is to learn a multi-task policy based on a linear representation while solving a sequence of tasks that may be adaptively chosen based on the agent's past behaviors. We frame the problem as a linearly parameterized contextual Markov decision process (MDP), where each task is specified by a context and the transition dynamics is context-independent, and we introduce a new completeness-style assumption on the representation which is sufficient to ensure the optimal multi-task policy is realizable under the linear representation. Under this assumption, we propose an algorithm, called UCB Lifelong Value Distillation (UCBlvd), that provably achieves sublinear regret for any sequence of tasks while using only sublinear planning calls. Specifically, for $K$ task episodes of horizon $H$, our algorithm has a regret bound $\tilde{\mathcal{O}}(\sqrt{(d^3+d^\prime d)H^4K})$ using $\mathcal{O}(dH\log(K))$ number of planning calls, where $d$ and $d^\prime$ are the feature dimensions of the dynamics and rewards, respectively. This theoretical guarantee implies that our algorithm can enable a lifelong learning agent to learn to internalize experiences into a multi-task policy and rapidly solve new tasks. | https://openreview.net/pdf/2e81dc2a2f2529b307345174708e2ffa49eb464e.pdf |
Link Prediction with Non-Contrastive Learning | https://openreview.net/forum?id=9Jaz4APHtWD | https://openreview.net/forum?id=9Jaz4APHtWD | William Shiao,Zhichun Guo,Tong Zhao,Evangelos E. Papalexakis,Yozen Liu,Neil Shah | ICLR 2023,Poster | Graph neural networks (GNNs) are prominent in the graph machine learning domain, owing to their strong performance across various tasks. A recent focal area is the space of graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Notably, many state-of-the-art graph SSL methods are contrastive methods, which use a combination of positive and negative samples to learn node representations. Owing to challenges in negative sampling (slowness and model sensitivity), recent literature introduced non-contrastive methods, which instead only use positive samples. Though such methods have shown promising performance in node-level tasks, their suitability for link prediction tasks, which are concerned with predicting link existence between pairs of nodes (and have broad applicability to recommendation systems contexts) is yet unexplored. In this work, we extensively evaluate the performance of existing non-contrastive methods for link prediction in both transductive and inductive settings. While most existing non-contrastive methods perform poorly overall, we find that, surprisingly, BGRL generally performs well in transductive settings. However, it performs poorly in the more realistic inductive settings where the model has to generalize to links to/from unseen nodes. We find that non-contrastive models tend to overfit to the training graph and use this analysis to propose T-BGRL, a novel non-contrastive framework that incorporates cheap corruptions to improve the generalization ability of the model. This simple modification strongly improves inductive performance in 5/6 of our datasets, with up to a 120% improvement in Hits@50 - all with comparable speed to other non-contrastive baselines, and up to $14\times$ faster than the best-performing contrastive baseline. Our work imparts interesting findings about non-contrastive learning for link prediction and paves the way for future researchers to further expand upon this area. | https://openreview.net/pdf/46cb48345cdfc877f29c5c93429f4dd9c06cbc74.pdf |
Distributed Differential Privacy in Multi-Armed Bandits | https://openreview.net/forum?id=cw8FeirkIfU | https://openreview.net/forum?id=cw8FeirkIfU | Sayak Ray Chowdhury,Xingyu Zhou | ICLR 2023,Poster | We consider the standard $K$-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work largely focus on achieving privacy using a shuffle protocol, where a batch of users data are randomly permuted before sending to a central server. This protocol achieves ($\epsilon,\delta$) or approximate-DP guarantee by sacrificing an additive $O\!\left(\!\frac{K\log T\sqrt{\log(1/\delta)}}{\epsilon}\!\right)\!$ factor in $T$-step cumulative regret. In contrast, the optimal privacy cost to achieve a stronger ($\epsilon,0$) or pure-DP guarantee under the widely used central trust model is only $\Theta\!\left(\!\frac{K\log T}{\epsilon}\!\right)\!$, where, however, a trusted server is required. In this work, we aim to obtain a pure-DP guarantee under distributed trust model while sacrificing no more regret than that under central trust model. We achieve this by designing a generic bandit algorithm based on successive arm elimination, where privacy is guaranteed by corrupting rewards with an equivalent discrete Laplace noise ensured by a secure computation protocol. We also show that our algorithm, when instantiated with Skellam noise and the secure protocol, ensures \emph{R\'{e}nyi differential privacy} -- a stronger notion than approximate DP -- under distributed trust model with a privacy cost of $O\!\left(\!\frac{K\sqrt{\log T}}{\epsilon}\!\right)\!$. Finally, as a by-product of our techniques, we also recover the best-known regret bounds for bandits under central and local models while using only \emph{discrete privacy noise}, which can avoid the privacy leakage due to floating point arithmetic of continuous noise on finite computers. | https://openreview.net/pdf/a27fa22bf764de3adf4fac72e6b5e551444389b4.pdf |
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity | https://openreview.net/forum?id=jClGv3Qjhb | https://openreview.net/forum?id=jClGv3Qjhb | Hongkang Li,Meng Wang,Sijia Liu,Pin-Yu Chen | ICLR 2023,Poster | Vision Transformers (ViTs) with self-attention modules have recently achieved great empirical success in many vision tasks. Due to non-convex interactions across layers, however, the theoretical learning and generalization analysis is mostly elusive. Based on a data model characterizing both label-relevant and label-irrelevant tokens, this paper provides the first theoretical analysis of training a three-layer ViT, i.e., one self-attention layer followed by a two-layer perceptron, for a classification task. We characterize the sample complexity to achieve a zero generalization error. Our sample complexity bound is positively correlated with the inverse of the fraction of label-relevant tokens, the token noise level, and the initial model error. We also prove that a training process using stochastic gradient descent (SGD) leads to a sparse attention map, which is a formal verification of the general intuition about the success of attention. Moreover, this paper indicates that a proper token sparsification can improve the test performance by removing label-irrelevant and/or noisy tokens, including spurious correlations. Empirical experiments on synthetic data and CIFAR-10 dataset justify our theoretical results and generalize to deeper ViTs. | https://openreview.net/pdf/31d1481db9db8d93532ef30aa9a64f3a33b188b1.pdf |
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions | https://openreview.net/forum?id=AjC0KBjiMu | https://openreview.net/forum?id=AjC0KBjiMu | Daniel D. Johnson,Ayoub El Hanchi,Chris J. Maddison | ICLR 2023,Poster | Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contrastive learning methods can be reinterpeted as learning kernel functions that approximate a fixed *positive-pair kernel*. We then prove that a simple representation obtained by combining this kernel with PCA provably minimizes the worst-case approximation error of linear predictors, under a straightforward assumption that positive pairs have similar labels. Our analysis is based on a decomposition of the target function in terms of the eigenfunctions of a positive-pair Markov chain, and a surprising equivalence between these eigenfunctions and the output of Kernel PCA. We give generalization bounds for downstream linear prediction using our kernel PCA representation, and show empirically on a set of synthetic tasks that applying kernel PCA to contrastive learning models can indeed approximately recover the Markov chain eigenfunctions, although the accuracy depends on the kernel parameterization as well as on the augmentation strength. | https://openreview.net/pdf/470d0d377fbd21359685cd8abe602339d5f71751.pdf |
Provably Auditing Ordinary Least Squares in Low Dimensions | https://openreview.net/forum?id=DlpCotqdTy | https://openreview.net/forum?id=DlpCotqdTy | Ankur Moitra,Dhruv Rohatgi | ICLR 2023,Poster | Auditing the stability of a machine learning model to small changes in the training procedure is critical for engendering trust in practical applications. For example, a model should not be overly sensitive to removing a small fraction of its training data. However, algorithmically validating this property seems computationally challenging, even for the simplest of models: Ordinary Least Squares (OLS) linear regression. Concretely, recent work defines the stability of a regression as the minimum number of samples that need to be removed so that rerunning the analysis overturns the conclusion (Broderick et al., 2020), specifically meaning that the sign of a particular coefficient of the OLS regressor changes. But the only known approach for estimating this metric, besides the obvious exponential-time algorithm, is a greedy heuristic that may produce severe overestimates and therefore cannot certify stability. We show that stability can be efficiently certified in the low-dimensional regime: when the number of covariates is a constant but the number of samples is large, there are polynomial-time algorithms for estimating (a fractional version of) stability, with provable approximation guarantees. Applying our algorithms to the Boston Housing dataset, we exhibit regression analyses where our estimator outperforms the greedy heuristic, and can successfully certify stability even in the regime where a constant fraction of the samples are dropped. | https://openreview.net/pdf/50c71abe6094fa12406a47a024a6b54be845582a.pdf |
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs | https://openreview.net/forum?id=Qamz7Q_Ta1k | https://openreview.net/forum?id=Qamz7Q_Ta1k | Sudhanshu Chanpuriya,Ryan A. Rossi,Sungchul Kim,Tong Yu,Jane Hoffswell,Nedim Lipka,Shunan Guo,Cameron N Musco | ICLR 2023,Poster | Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First, many methods assume time to be discretized, so if the time data is continuous, the user must determine the discretization and discard precise time information. Second, edge representations can only be calculated indirectly from the nodes, which may be suboptimal for tasks like edge classification. We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions. From this derived graph, edge representations for the original network can be computed with efficient classical methods. The simplicity of this approach facilitates explicit theoretical analysis: we can constructively show the effectiveness of our method's representations for a natural synthetic model of temporal networks. Empirical results on real-world networks demonstrate our method's efficacy and efficiency on both link classification and prediction. | https://openreview.net/pdf/90f557dbe9c9290881d4da0fddf557a84bd2da96.pdf |
Neural DAG Scheduling via One-Shot Priority Sampling | https://openreview.net/forum?id=WL8FlAugqQ | https://openreview.net/forum?id=WL8FlAugqQ | Wonseok Jeon,Mukul Gagrani,Burak Bartan,Weiliang Will Zeng,Harris Teague,Piero Zappi,Christopher Lott | ICLR 2023,Poster | We consider the problem of scheduling operations/nodes, the dependency among which is characterized by a Directed Acyclic Graph (DAG). Due to its NP-hard nature, heuristic algorithms were traditionally used to acquire reasonably good solutions, and more recent works have proposed Machine Learning (ML) heuristics that can generalize to unseen graphs and outperform the non-ML heuristics. However, it is computationally costly to generate solutions using existing ML schedulers since they adopt the episodic reinforcement learning framework that necessitates multi-round neural network processing. We propose a novel ML scheduler that uses a one-shot neural network encoder to sample node priorities which are converted by list scheduling to the final schedules. Since the one-shot encoder can efficiently sample the priorities in parallel, our algorithm runs significantly faster than existing ML baselines and has comparable run time with the fast traditional heuristics. We empirically show that our algorithm generates better schedules than both non-neural and neural baselines across various real-world and synthetic scheduling tasks. | https://openreview.net/pdf/20210ea69d6b5ccead90b146eeb53cf34ed79f34.pdf |
Meta Temporal Point Processes | https://openreview.net/forum?id=QZfdDpTX1uM | https://openreview.net/forum?id=QZfdDpTX1uM | Wonho Bae,Mohamed Osama Ahmed,Frederick Tung,Gabriel L. Oliveira | ICLR 2023,Poster | A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs). We introduce context sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we also introduce local history matching to help learn more informative features. We demonstrate the potential of the proposed method on popular public benchmark datasets and tasks, and compare with state-of-the-art TPP methods. | https://openreview.net/pdf/010e244d35bad1b1979dca9dc65f74e8ce5fa492.pdf |
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning | https://openreview.net/forum?id=flap0Bo6TK_ | https://openreview.net/forum?id=flap0Bo6TK_ | Zehao Niu,Mihai Anitescu,Jie Chen | ICLR 2023,Poster | Gaussian processes (GPs) are an attractive class of machine learning models because of their simplicity and flexibility as building blocks of more complex Bayesian models. Meanwhile, graph neural networks (GNNs) emerged recently as a promising class of models for graph-structured data in semi-supervised learning and beyond. Their competitive performance is often attributed to a proper capturing of the graph inductive bias. In this work, we introduce this inductive bias into GPs to improve their predictive performance for graph-structured data. We show that a prominent example of GNNs, the graph convolutional network, is equivalent to some GP when its layers are infinitely wide; and we analyze the kernel universality and the limiting behavior in depth. We further present a programmable procedure to compose covariance kernels inspired by this equivalence and derive example kernels corresponding to several interesting members of the GNN family. We also propose a computationally efficient approximation of the covariance matrix for scalable posterior inference with large-scale data. We demonstrate that these graph-based kernels lead to competitive classification and regression performance, as well as advantages in computation time, compared with the respective GNNs. | https://openreview.net/pdf/a4aa62e7de51e362d381dc350f1607b055cbaf0f.pdf |
Deconstructing Distributions: A Pointwise Framework of Learning | https://openreview.net/forum?id=9IaN4FkVSR1 | https://openreview.net/forum?id=9IaN4FkVSR1 | Gal Kaplun,Nikhil Ghosh,Saurabh Garg,Boaz Barak,Preetum Nakkiran | ICLR 2023,Poster | In machine learning, we traditionally evaluate the performance of a single model, averaged over a collection of test inputs. In this work, we propose a new approach: we measure the performance of a collection of models when evaluated at *single input point*. Specifically, we study a point's *profile*: the relationship between models' average performance on the test distribution and their pointwise performance on this individual point. We find that profiles can yield new insights into the structure of both models and data---in and out-of-distribution. For example, we empirically show that real data distributions consist of points with qualitatively different profiles. On one hand, there are ``compatible'' points with strong correlation between the pointwise and average performance. On the other hand, there are points with weak and even *negative* correlation: cases where improving overall model accuracy actually *hurts* performance on these inputs. As an application, we use profiles to construct a dataset we call CIFAR-10-NEG: a subset of CINIC-10 such that for standard models, accuracy on CIFAR-10-NEG is *negatively correlated* with CIFAR-10 accuracy. Illustrating for the first time an OOD dataset that completely inverts ``accuracy-on-the-line'' (Miller et al., 2021). | https://openreview.net/pdf/7cd5fac151d9b675ecd5b5718a6379fbbaa12350.pdf |
Diffusion Models for Causal Discovery via Topological Ordering | https://openreview.net/forum?id=Idusfje4-Wq | https://openreview.net/forum?id=Idusfje4-Wq | Pedro Sanchez,Xiao Liu,Alison Q O'Neil,Sotirios A. Tsaftaris | ICLR 2023,Poster | Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise (ANM). Even with strong assumptions, causal discovery involves an expensive search problem over the space of directed acyclic graphs (DAGs). \emph{Topological ordering} approaches reduce the optimisation space of causal discovery by searching over a permutation rather than graph space.
For ANMs, the \emph{Hessian} of the data log-likelihood can be used for finding leaf nodes in a causal graph, allowing its topological ordering. However, existing computational methods for obtaining the Hessian still do not scale as the number of variables and the number of samples are increased. Therefore, inspired by recent innovations in diffusion probabilistic models (DPMs), we propose \emph{DiffAN}, a topological ordering algorithm that leverages DPMs for learning a Hessian function. We introduce theory for updating the learned Hessian without re-training the neural network, and we show that computing with a subset of samples gives an accurate approximation of the ordering, which allows scaling to datasets with more samples and variables. We show empirically that our method scales exceptionally well to datasets with up to $500$ nodes and up to $10^5$ samples while still performing on par over small datasets with state-of-the-art causal discovery methods.
Implementation is available at \url{https://github.com/vios-s/DiffAN} . | https://openreview.net/pdf/6507802ca7e95e60abfdd3910aaefa5450193400.pdf |
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions | https://openreview.net/forum?id=eb_cpjZZ3GH | https://openreview.net/forum?id=eb_cpjZZ3GH | Jeremy Ocampo,Matthew Alexander Price,Jason McEwen | ICLR 2023,Poster | No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cost of equivariance. We develop a hybrid discrete-continuous (DISCO) group convolution that is simultaneously equivariant and computationally scalable to high-resolution. While our framework can be applied to any compact group, we specialize to the sphere. Our DISCO spherical convolutions exhibit $\text{SO}(3)$ rotational equivariance, where $\text{SO}(n)$ is the special orthogonal group representing rotations in $n$-dimensions. When restricting rotations of the convolution to the quotient space $\text{SO}(3)/\text{SO}(2)$ for further computational enhancements, we recover a form of asymptotic $\text{SO}(3)$ rotational equivariance. Through a sparse tensor implementation we achieve linear scaling in number of pixels on the sphere for both computational cost and memory usage. For 4k spherical images we realize a saving of $10^9$ in computational cost and $10^4$ in memory usage when compared to the most efficient alternative equivariant spherical convolution. We apply the DISCO spherical CNN framework to a number of benchmark dense-prediction problems on the sphere, such as semantic segmentation and depth estimation, on all of which we achieve the state-of-the-art performance. | https://openreview.net/pdf/06e1637d0d7663914cdffbde335804a2f37b878c.pdf |
Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic | https://openreview.net/forum?id=Hu4r-dedqR0 | https://openreview.net/forum?id=Hu4r-dedqR0 | Zijun Wu,Zi Xuan Zhang,Atharva Naik,Zhijian Mei,Mauajama Firdaus,Lili Mou | ICLR 2023,Poster | Natural language inference (NLI) aims to determine the logical relationship between two sentences, such as Entailment, Contradiction, and Neutral. In recent years, deep learning models have become a prevailing approach to NLI, but they lack interpretability and explainability. In this work, we address the explainability of NLI by weakly supervised logical reasoning, and propose an Explainable Phrasal Reasoning (EPR) approach. Our model first detects phrases as the semantic unit and aligns corresponding phrases in the two sentences. Then, the model predicts the NLI label for the aligned phrases, and induces the sentence label by fuzzy logic formulas. Our EPR is almost everywhere differentiable and thus the system can be trained end to end. In this way, we are able to provide explicit explanations of phrasal logical relationships in a weakly supervised manner. We further show that such reasoning results help textual explanation generation. | https://openreview.net/pdf/028e5f4cb530b4105893cd7dcabbd0eae72dc100.pdf |
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability | https://openreview.net/forum?id=462z-gLgSht | https://openreview.net/forum?id=462z-gLgSht | Cian Eastwood,Andrei Liviu Nicolicioiu,Julius Von Kügelgen,Armin Kekić,Frederik Träuble,Andrea Dittadi,Bernhard Schölkopf | ICLR 2023,Poster | In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativeness (I). In this work, we first connect this DCI framework to two common notions of linear and nonlinear identifiability, thereby establishing a formal link between disentanglement and the closely-related field of independent component analysis. We then propose an extended DCI-ES framework with two new measures of representation quality—explicitness (E) and size (S)—and point out how D and C can be computed for black-box predictors. Our main idea is that the functional capacity required to use a representation is an important but thus-far neglected aspect of representation quality, which we quantify using explicitness or ease-of-use (E). We illustrate the relevance of our extensions on the MPI3D and Cars3D datasets. | https://openreview.net/pdf/df1d40b1c077f27e1bc6b520a058ce60018e288e.pdf |
Faster federated optimization under second-order similarity | https://openreview.net/forum?id=ElC6LYO4MfD | https://openreview.net/forum?id=ElC6LYO4MfD | Ahmed Khaled,Chi Jin | ICLR 2023,Poster | Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider finite-sum federated optimization under a second-order function similarity condition and strong convexity, and propose two new algorithms: SVRP and Catalyzed SVRP. This second-order similarity condition has grown popular recently, and is satisfied in many applications including distributed statistical learning and differentially private empirical risk minimization. The first algorithm, SVRP, combines approximate stochastic proximal point evaluations, client sampling, and variance reduction. We show that SVRP is communication efficient and achieves superior performance to many existing algorithms when function similarity is high enough. Our second algorithm, Catalyzed SVRP, is a Catalyst-accelerated variant of SVRP that achieves even better performance and uniformly improves upon existing algorithms for federated optimization under second-order similarity and strong convexity. In the course of analyzing these algorithms, we provide a new analysis of the Stochastic Proximal Point Method (SPPM) that might be of independent interest. Our analysis of SPPM is simple, allows for approximate proximal point evaluations, does not require any smoothness assumptions, and shows a clear benefit in communication complexity over ordinary distributed stochastic gradient descent. | https://openreview.net/pdf/e1863fb8fa839d7726613016245ed5b8e27ff6f2.pdf |
Mutual Partial Label Learning with Competitive Label Noise | https://openreview.net/forum?id=EUrxG8IBCrC | https://openreview.net/forum?id=EUrxG8IBCrC | Yan Yan,Yuhong Guo | ICLR 2023,Poster | Partial label learning (PLL) is an important weakly supervised learning problem, where each training instance is associated with a set of candidate labels that include both the true label and additional noisy labels. Most existing PLL methods assume the candidate noisy labels are randomly chosen, which hardly holds in real-world learning scenarios. In this paper, we consider a more realistic PLL scenario with competitive label noise that is more difficult to distinguish from the true label than the random label noise. We propose a novel Mutual Learning based PLL approach named ML-PLL to address this challenging problem. ML-PLL learns a prediction network based classifier and a class-prototype based classifier cooperatively through interactive mutual learning and label correction. Moreover, we use a transformation network to model the association relationships between the true label and candidate labels, and learn it together with the prediction network to match the observed candidate labels in the training data and enhance label correction. Extensive experiments are conducted on several benchmark PLL datasets, and the proposed ML-PLL approach demonstrates state-of-the-art performance for partial label learning. | https://openreview.net/pdf/d4d4aafdbb05dbe6ade49564f966110dd5f1e18a.pdf |
Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment | https://openreview.net/forum?id=jpq0qHggw3t | https://openreview.net/forum?id=jpq0qHggw3t | Yan Yan,Yuhong Guo | ICLR 2023,Poster | Partial label learning (PLL) tackles the problem where each instance is associated with a set of candidate labels, only one of which is the ground-truth label. Most existing PLL approaches assume that both the training and test sets share an identical data distribution. However, this assumption does not hold in many real-world scenarios where the training and test data come from different distributions. In this paper, we formalize this learning scenario as a new problem called partial label unsupervised domain adaptation (PLUDA). To address this challenging PLUDA problem, we propose a novel Prototype Alignment based PLUDA method named PAPLUDA, which dynamically refines the pseudo-labels of instances from both the source and target domains by consulting the outputs of a teacher-student model in a moving-average manner, and bridges the cross-domain discrepancy through inter-domain class-prototype alignment. In addition, a teacher-student model based contrastive regularization is deployed to enhance prediction stability and hence improve the class-prototypes in both domains for PLUDA. Comprehensive experimental results demonstrate that PAPLUDA achieves state-of-the-art performance on the widely used benchmark datasets. | https://openreview.net/pdf/dd2d4c58a30acac15a00d195cf88a31841339611.pdf |
simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing | https://openreview.net/forum?id=9HiGqC9C-KA | https://openreview.net/forum?id=9HiGqC9C-KA | Zitao Liu,Qiongqiong Liu,Jiahao Chen,Shuyan Huang,Weiqi Luo | ICLR 2023,Poster | Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol \citep{liu2022pykt}, there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821 \citep{minn2018deep,yeung2018addressing}. Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named \textsc{simpleKT}. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at \url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}. | https://openreview.net/pdf/d9869b82d0c8374bd652a9c12018e7a37a26ff2d.pdf |
Weighted Ensemble Self-Supervised Learning | https://openreview.net/forum?id=CL-sVR9pvF | https://openreview.net/forum?id=CL-sVR9pvF | Yangjun Ruan,Saurabh Singh,Warren Richard Morningstar,Alexander A Alemi,Sergey Ioffe,Ian Fischer,Joshua V. Dillon | ICLR 2023,Poster | Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art few-shot and supervised learning performance. In this paper, we explore how ensemble methods can improve recent SSL techniques by developing a framework that permits data-dependent weighted cross-entropy losses. We refrain from ensembling the representation backbone; this choice yields an efficient ensemble method that incurs a small training cost and requires no architectural changes or computational overhead to downstream evaluation. The effectiveness of our method is demonstrated with two state-of-the-art SSL methods, DINO (Caron et al., 2021) and MSN (Assran et al., 2022). Our method outperforms both in multiple evaluation metrics on ImageNet-1K, particularly in the few-shot setting. We explore several weighting schemes and find that those which increase the diversity of ensemble heads lead to better downstream evaluation results. Thorough experiments yield improved prior art baselines which our method still surpasses; e.g., our overall improvement with MSN ViT-B/16 is 3.9 p.p. for 1-shot learning. | https://openreview.net/pdf/c3bd68473e3729fbbb9b7485d2dfe3af068d28c4.pdf |
Guiding Safe Exploration with Weakest Preconditions | https://openreview.net/forum?id=zzqBoIFOQ1 | https://openreview.net/forum?id=zzqBoIFOQ1 | Greg Anderson,Swarat Chaudhuri,Isil Dillig | ICLR 2023,Poster | In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic weakest preconditions to achieve a more precise safety analysis than existing tools without unduly impacting the training process. We evaluate the approach on a suite of continuous control benchmarks and show that it can achieve comparable performance to existing safe learning techniques while incurring fewer safety violations. Additionally, we present theoretical results showing that SPICE converges to the optimal safe policy under reasonable assumptions. | https://openreview.net/pdf/31fba2ce53b7314f1c3b3ec7719c818d82414a6d.pdf |
Is a Caption Worth a Thousand Images? A Study on Representation Learning | https://openreview.net/forum?id=cYijsVZhb5 | https://openreview.net/forum?id=cYijsVZhb5 | Shibani Santurkar,Yann Dubois,Rohan Taori,Percy Liang,Tatsunori Hashimoto | ICLR 2023,Poster | The development of CLIP [Radford et al., 2021] has sparked a debate on whether adding language supervision can yield vision models with more transferable representations than traditional image-only methods. Our work studies this question through a carefully controlled comparison of two approaches, in terms of their ability to learn representations that generalize to downstream classification tasks. We find that when the pre-training data meets certain criteria---it is sufficiently large and contains descriptive captions with low variability----image-only methods do not match CLIP's performance even when they are trained with more image data. However, contrary to what one might expect, there are practical settings in which these criteria are not met, wherein added supervision through captions is actually detrimental.
Motivated by our findings, we devise simple data and algorithmic interventions to improve the transfer performance of CLIP-style models. | https://openreview.net/pdf/4f51fa37cd1664b17e82c6e35385e764cc1b606a.pdf |
Parameter-Efficient Fine-Tuning Design Spaces | https://openreview.net/forum?id=XSRSWxyJIC | https://openreview.net/forum?id=XSRSWxyJIC | Jiaao Chen,Aston Zhang,Xingjian Shi,Mu Li,Alex Smola,Diyi Yang | ICLR 2023,Poster | Parameter-efficient fine-tuning aims to achieve comparable performances of fine-tuning with much fewer trainable parameters. Recently, various tuning strategies (e.g., Adapters, Prefix Tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted separately, and it remains unclear whether certain design patterns exist for parameter-efficient fine-tuning. Thus, we present a parameter-efficient fine-tuning design paradigm and discover design patterns that are applicable to different experimental settings. Instead of focusing on designing another individual tuning strategy, we introduce parameter-efficient fine-tuning design spaces that parameterize tuning structures and tuning strategies. Specifically, any design space is characterized by four components: layer grouping, trainable parameter allocation, tunable groups, and strategy assignment. Our comprehensive empirical study leads to the discovery of design patterns: (i) grouping layers in a spindle pattern, (ii) uniformly allocating the number of trainable parameters to layers, (ii) tuning all the groups, and (iv) tuning different groups with proper strategies. Our discovered design patterns result in new parameter-efficient fine-tuning methods. Experiments show that these methods consistently outperform investigated parameter-efficient fine-tuning strategies across different backbone models and different tasks in natural language processing. | https://openreview.net/pdf/4c4f8f1aff3fb1f79e893b2ca5430c1207b90de4.pdf |
Concept Gradient: Concept-based Interpretation Without Linear Assumption | https://openreview.net/forum?id=_01dDd3f78 | https://openreview.net/forum?id=_01dDd3f78 | Andrew Bai,Chih-Kuan Yeh,Neil Y.C. Lin,Pradeep Kumar Ravikumar,Cho-Jui Hsieh | ICLR 2023,Poster | Concept-based interpretations of black-box models are often more intuitive for humans to understand. The most widely adopted approach for concept-based, gradient interpretation is Concept Activation Vector (CAV). CAV relies on learning a linear relation between some latent representation of a given model and concepts. The premise of meaningful concepts lying in a linear subspace of model layers is usually implicitly assumed but does not hold true in general. In this work we proposed Concept Gradient (CG), which extends concept-based, gradient interpretation methods to non-linear concept functions. We showed that for a general (potentially non-linear) concept, we can mathematically measure how a small change of concept affects the model’s prediction, which is an extension of gradient-based interpretation to the concept space. We demonstrated empirically that CG outperforms CAV in attributing concept importance on real world datasets and performed case study on a medical dataset. The code is available at github.com/jybai/concept-gradients. | https://openreview.net/pdf/89419e0a5c44f295ace102a51f36ee249c1378a9.pdf |
Constraining Representations Yields Models That Know What They Don't Know | https://openreview.net/forum?id=1w_Amtk67X | https://openreview.net/forum?id=1w_Amtk67X | Joao Monteiro,Pau Rodriguez,Pierre-Andre Noel,Issam H. Laradji,David Vazquez | ICLR 2023,Poster | A well-known failure mode of neural networks is that they may confidently return erroneous predictions. Such unsafe behaviour is particularly frequent when the use case slightly differs from the training context, and/or in the presence of an adversary. This work presents a novel direction to address these issues in a broad, general manner: imposing class-aware constraints on a model's internal activation patterns. Specifically, we assign to each class a unique, fixed, randomly-generated binary vector - hereafter called class code - and train the model so that its cross-depths activation patterns predict the appropriate class code according to the input sample's class. The resulting predictors are dubbed total activation classifiers (TAC), and TACs may either be trained from scratch, or used with negligible cost as a thin add-on on top of a frozen, pre-trained neural network. The distance between a TAC's activation pattern and the closest valid code acts as an additional confidence score, besides the default unTAC'ed prediction head's. In the add-on case, the original neural network's inference head is completely unaffected (so its accuracy remains the same) but we now have the option to use TAC's own confidence and prediction when determining which course of action to take in an hypothetical production workflow. In particular, we show that TAC strictly improves the value derived from models allowed to reject/defer. We provide further empirical evidence that TAC works well on multiple types of architectures and data modalities and that it is at least as good as state-of-the-art alternative confidence scores derived from existing models. | https://openreview.net/pdf/9617b430ccc1f891ee6b1addaa9937d9c5383bc7.pdf |
An Extensible Multi-modal Multi-task Object Dataset with Materials | https://openreview.net/forum?id=n70oyIlS4g | https://openreview.net/forum?id=n70oyIlS4g | Trevor Scott Standley,Ruohan Gao,Dawn Chen,Jiajun Wu,Silvio Savarese | ICLR 2023,Poster | We present EMMa, an Extensible, Multimodal dataset of Amazon product listings that contains rich Material annotations. It contains more than 2.8 million objects, each with image(s), listing text, mass, price, product ratings, and position in Amazon’s product-category taxonomy. We also design a comprehensive taxonomy of 182 physical materials (e.g., Plastic → Thermoplastic → Acrylic). Objects areannotated with one or more materials from this taxonomy. With the numerous attributes available for each object, we develop a Smart Labeling framework to quickly add new binary labels to all objects with very little manual labeling effort, making the dataset extensible. Each object attribute in our dataset can be included in either the model inputs or outputs, leading to combinatorial possibilities in task configurations. For example, we can train a model to predict the object category from the listing text, or the mass and price from the product listing image. EMMa offers a new benchmark for multi-task learning in computer vision and NLP, and allows practitioners to efficiently add new tasks and object attributes at scale. | https://openreview.net/pdf/e8afd23963050ab7468dcd74394d5b194f47d7e7.pdf |
Sampling with Mollified Interaction Energy Descent | https://openreview.net/forum?id=zWy7dqOcel | https://openreview.net/forum?id=zWy7dqOcel | Lingxiao Li,qiang liu,Anna Korba,Mikhail Yurochkin,Justin Solomon | ICLR 2023,Poster | Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper, we present a new optimization-based method for sampling called mollified interaction energy descent (MIED). MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs). These energies rely on mollifier functions---smooth approximations of the Dirac delta originated from PDE theory. We show that as the mollifier approaches the Dirac delta, the MIE converges to the chi-square divergence with respect to the target measure and the gradient flow of the MIE agrees with that of the chi-square divergence. Optimizing this energy with proper discretization yields a practical first-order particle-based algorithm for sampling in both unconstrained and constrained domains. We show experimentally that for unconstrained sampling problems our algorithm performs on par with existing particle-based algorithms like SVGD, while for constrained sampling problems our method readily incorporates constrained optimization techniques to handle more flexible constraints with strong performance compared to alternatives.
| https://openreview.net/pdf/858241f6c2561f8e00321e85c01eb1c15f18be15.pdf |
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability | https://openreview.net/forum?id=nhKHA59gXz | https://openreview.net/forum?id=nhKHA59gXz | Alex Damian,Eshaan Nichani,Jason D. Lee | ICLR 2023,Poster | Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness $S(\theta)$, is bounded by $2/\eta$, training is "stable" and the training loss decreases monotonically. Recent works, however, have observed that this assumption does not hold when training modern neural networks with full batch or large batch gradient descent. Most recently, Cohen at al. (2021) detailed two important phenomena. The first, dubbed \emph{progressive sharpening}, is that the sharpness steadily increases throughout training until it reaches the instability cutoff $2/\eta$. The second, dubbed \emph{edge of stability}, is that the sharpness hovers at $2/\eta$ for the remainder of training while the loss continues decreasing, albeit non-monotonically. We demonstrate that, far from being chaotic, the dynamics of gradient descent at the edge of stability can be captured by a cubic Taylor expansion: as the iterates diverge in direction of the top eigenvector of the Hessian due to instability, the cubic term in the local Taylor expansion of the loss function causes the curvature to decrease until stability is restored. This property, which we call \emph{self-stabilization}, is a general property of gradient descent and explains its behavior at the edge of stability. A key consequence of self-stabilization is that gradient descent at the edge of stability implicitly follows \emph{projected} gradient descent (PGD) under the constraint $S(\theta) \le 2/\eta$. Our analysis provides precise predictions for the loss, sharpness, and deviation from the PGD trajectory throughout training, which we verify both empirically in a number of standard settings and theoretically under mild conditions. Our analysis uncovers the mechanism for gradient descent's implicit bias towards stability. | https://openreview.net/pdf/5ce767b4f951bb7db5b2c04af424d1c59c1e9387.pdf |
TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs | https://openreview.net/forum?id=_X12NmQKvX | https://openreview.net/forum?id=_X12NmQKvX | Siheng Xiong,Yuan Yang,Faramarz Fekri,James Clayton Kerce | ICLR 2023,Poster | Compared with static knowledge graphs, temporal knowledge graphs (tKG), which can capture the evolution and change of information over time, are more realistic and general. However, due to the complexity that the notion of time introduces to the learning of the rules, an accurate graph reasoning, e.g., predicting new links between entities, is still a difficult problem. In this paper, we propose TILP, a differentiable framework for temporal logical rules learning. By designing a constrained random walk mechanism and the introduction of temporal operators, we ensure the efficiency of our model. We present temporal features modeling in tKG, e.g., recurrence, temporal order, interval between pair of relations, and duration, and incorporate it into our learning process. We compare TILP with state-of-the-art methods on two benchmark datasets. We show that our proposed framework can improve upon the performance of baseline methods while providing interpretable results. In particular, we consider various scenarios in which training samples are limited, data is biased, and the time range between training and inference are different. In all these cases, TILP works much better than the state-of-the-art methods. | https://openreview.net/pdf/b837b878ac08cd49f6f79660b6bb8703badd9c4d.pdf |
Open-Vocabulary Object Detection upon Frozen Vision and Language Models | https://openreview.net/forum?id=MIMwy4kh9lf | https://openreview.net/forum?id=MIMwy4kh9lf | Weicheng Kuo,Yin Cui,Xiuye Gu,AJ Piergiovanni,Anelia Angelova | ICLR 2023,Poster | We present F-VLM, a simple open-vocabulary object detection method built uponFrozenVision andLanguageModels. F-VLM simplifies the current multi-stage training pipeline by eliminating the need for knowledge distillation or detection-tailored pretraining. Surprisingly, we observe that a frozen VLM: 1) retains the locality-sensitive features necessary for detection, and 2) is a strong region classifier. We finetune only the detector head and combine the detector and VLM outputs for each region at inference time. F-VLM shows compelling scaling behavior and achieves +6.5 mask AP improvement over the previous state of theart on novel categories of LVIS open-vocabulary detection benchmark. In addition, we demonstrate very competitive results on COCO open-vocabulary detection benchmark and cross-dataset transfer detection, in addition to significant training speed-up and compute savings. Code will be released.
| https://openreview.net/pdf/7d9f5b2672aa5a14a75728dc195e5033b0f308aa.pdf |
Revisiting the Assumption of Latent Separability for Backdoor Defenses | https://openreview.net/forum?id=_wSHsgrVali | https://openreview.net/forum?id=_wSHsgrVali | Xiangyu Qi,Tinghao Xie,Yiming Li,Saeed Mahloujifar,Prateek Mittal | ICLR 2023,Poster | Recent studies revealed that deep learning is susceptible to backdoor poisoning attacks. An adversary can embed a hidden backdoor into a model to manipulate its predictions by only modifying a few training data, without controlling the training process. Currently, a tangible signature has been widely observed across a diverse set of backdoor poisoning attacks --- models trained on a poisoned dataset tend to learn separable latent representations for poison and clean samples. This latent separation is so pervasive that a family of backdoor defenses directly take it as a default assumption (dubbed latent separability assumption), based on which to identify poison samples via cluster analysis in the latent space. An intriguing question consequently follows: is the latent separation unavoidable for backdoor poisoning attacks? This question is central to understanding whether the assumption of latent separability provides a reliable foundation for defending against backdoor poisoning attacks. In this paper, we design adaptive backdoor poisoning attacks to present counter-examples against this assumption. Our methods include two key components: (1) a set of trigger-planted samples correctly labeled to their semantic classes (other than the target class) that can regularize backdoor learning; (2) asymmetric trigger planting strategies that help to boost attack success rate (ASR) as well as to diversify latent representations of poison samples. Extensive experiments on benchmark datasets verify the effectiveness of our adaptive attacks in bypassing existing latent separation based backdoor defenses. Moreover, our attacks still maintain a high attack success rate with negligible clean accuracy drop. Our studies call for defense designers to take caution when leveraging latent separation as an assumption in their defenses. Our codes are available at https://github.com/Unispac/Circumventing-Backdoor-Defenses. | https://openreview.net/pdf/4c94fe40e30925694ed4ecc84bacd2fc7543b21c.pdf |
Restricted Strong Convexity of Deep Learning Models with Smooth Activations | https://openreview.net/forum?id=PINRbk7h01 | https://openreview.net/forum?id=PINRbk7h01 | Arindam Banerjee,Pedro Cisneros-Velarde,Libin Zhu,Misha Belkin | ICLR 2023,Poster | We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the ``near initialization'' perspective, we shed considerable new light on the problem. In particular, we make two key technical contributions for such models with $L$ layers, $m$ width, and $\sigma_0^2$ initialization variance. First, for suitable $\sigma_0^2$, we establish a $O(\frac{\text{poly}(L)}{\sqrt{m}})$ upper bound on the spectral norm of the Hessian of such models, considerably sharpening prior results. Second, we introduce a new analysis of optimization based on Restricted Strong Convexity (RSC) which holds as long as the squared norm of the average gradient of predictors is $\Omega(\frac{\text{poly}(L)}{\sqrt{m}})$ for the square loss. We also present results for more general losses. The RSC based analysis does not need the ``near initialization" perspective and guarantees geometric convergence for gradient descent (GD). To the best of our knowledge, ours is the first result on establishing geometric convergence of GD based on RSC for deep learning models, thus becoming an alternative sufficient condition for convergence that does not depend on the widely-used Neural Tangent Kernel (NTK). We share preliminary experimental results supporting our theoretical advances. | https://openreview.net/pdf/896805d4e8de1abe529f0fcfcfe247092bfdcdfa.pdf |
Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts | https://openreview.net/forum?id=kUmdmHxK5N | https://openreview.net/forum?id=kUmdmHxK5N | Rui Wang,Yihe Dong,Sercan O Arik,Rose Yu | ICLR 2023,Poster | Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (DNNs). In this paper, we propose a novel deep sequence model based on the Koopman theory for time series forecasting: Koopman Neural Forecaster (KNF) that leverages DNNs to learn the linear Koopman space and the coefficients of chosen measurement functions. KNF imposes appropriate inductive biases for improved robustness against distributional shifts, employing both a global operator to learn shared characteristics and a local operator to capture changing dynamics, as well as a specially-designed feedback loop to continuously update the learnt operators over time for rapidly varying behaviors. We demonstrate that KNF achieves superior performance compared to the alternatives, on multiple time series datasets that are shown to suffer from distribution shifts. | https://openreview.net/pdf/d87b4c4afa8afd01a326ff318affa6be941bc7f1.pdf |
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning | https://openreview.net/forum?id=C1ns08q9jZ | https://openreview.net/forum?id=C1ns08q9jZ | Namyong Park,Ryan A. Rossi,Nesreen Ahmed,Christos Faloutsos | ICLR 2023,Poster | Given a graph learning task, such as link prediction, on a new graph, how can we select the best method as well as its hyperparameters (collectively called a model) without having to train or evaluate any model on the new graph? Model selection for graph learning has been largely ad hoc. A typical approach has been to apply popular methods to new datasets, but this is often suboptimal. On the other hand, systematically comparing models on the new graph quickly becomes too costly, or even impractical. In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called MetaGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations. To quantify similarities across a wide variety of graphs, we introduce specialized meta-graph features that capture the structural characteristics of a graph. Then we design G-M network, which represents the relations among graphs and models, and develop a graph-based meta-learner operating on this G-M network, which estimates the relevance of each model to different graphs. Extensive experiments show that using MetaGL to select a model for the new graph greatly outperforms several existing meta-learning techniques tailed for graph learning model selection (up to 47% better), while being extremely fast at test time (∼1 sec). | https://openreview.net/pdf/57e37d336a0a3d192f593fb304990412d21afbe0.pdf |
Minimum Description Length Control | https://openreview.net/forum?id=oX3tGygjW1q | https://openreview.net/forum?id=oX3tGygjW1q | Ted Moskovitz,Ta-Chu Kao,Maneesh Sahani,Matthew Botvinick | ICLR 2023,Poster | We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced and then distills it into a simpler representation which facilitates faster convergence and generalization to new tasks. In doing so, MDL-C naturally balances adaptation to each task with epistemic uncertainty about the task distribution. We motivate MDL-C via formal connections between the MDL principle and Bayesian inference, derive theoretical performance guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete and high-dimensional continuous control tasks. | https://openreview.net/pdf/5871e6989fbc1c5944dd1b97644f24a796d43f39.pdf |
PerFedMask: Personalized Federated Learning with Optimized Masking Vectors | https://openreview.net/forum?id=hxEIgUXLFF | https://openreview.net/forum?id=hxEIgUXLFF | Mehdi Setayesh,Xiaoxiao Li,Vincent W.S. Wong | ICLR 2023,Poster | Recently, various personalized federated learning (FL) algorithms have been proposed to tackle data heterogeneity. To mitigate device heterogeneity, a common approach is to use masking. In this paper, we first show that using random masking can lead to a bias in the obtained solution of the learning model. To this end, we propose a personalized FL algorithm with optimized masking vectors called PerFedMask. In particular, PerFedMask facilitates each device to obtain its optimized masking vector based on its computational capability before training. Fine-tuning is performed after training. PerFedMask is a generalization of a recently proposed personalized FL algorithm, FedBABU (Oh et al., 2022). PerFedMask can be combined with other FL algorithms including HeteroFL (Diao et al., 2021) and Split-Mix FL (Hong et al., 2022). Results based on CIFAR-10 and CIFAR-100 datasets show that the proposed PerFedMask algorithm provides a higher test accuracy after fine-tuning and lower average number of trainable parameters when compared with six existing state-of-the-art FL algorithms in the literature. The codes are available at https://github.com/MehdiSet/PerFedMask. | https://openreview.net/pdf/03f646c79d86851f00a6e806f8984dd0ec3512c9.pdf |
Variational Latent Branching Model for Off-Policy Evaluation | https://openreview.net/forum?id=3VFQfAG3vwi | https://openreview.net/forum?id=3VFQfAG3vwi | Qitong Gao,Ge Gao,Min Chi,Miroslav Pajic | ICLR 2023,Poster | Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods face two key challenges. First, as offline trajectories are usually fixed, they tend to cover limited state and action space. Second, the performance of model-based methods can be sensitive to the initialization of their parameters. In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled. Specifically, VLBM leverages and extends the variational inference framework with the recurrent state alignment (RSA), which is designed to capture as much information underlying the limited training data, by smoothing out the information flow between the variational (encoding) and generative (decoding) part of VLBM. Moreover, we also introduce the branching architecture to improve the model’s robustness against randomly initialized model weights. The effectiveness of the VLBM is evaluated on the deep OPE (DOPE) benchmark, from which the training trajectories are designed to result in varied coverage of the state-action space. We show that the VLBM outperforms existing state-of-the-art OPE methods in general. | https://openreview.net/pdf/59d490c9944a87d96bea36cd12136115535559d0.pdf |
Tuning Frequency Bias in Neural Network Training with Nonuniform Data | https://openreview.net/forum?id=oLIZ2jGTiv | https://openreview.net/forum?id=oLIZ2jGTiv | Annan Yu,Yunan Yang,Alex Townsend | ICLR 2023,Poster | Small generalization errors of over-parameterized neural networks (NNs) can be partially explained by the frequency biasing phenomenon, where gradient-based algorithms minimize the low-frequency misfit before reducing the high-frequency residuals. Using the Neural Tangent Kernel (NTK), one can provide a theoretically rigorous analysis for training where data are drawn from constant or piecewise-constant probability densities. Since most training data sets are not drawn from such distributions, we use the NTK model and a data-dependent quadrature rule to theoretically quantify the frequency biasing of NN training given fully nonuniform data. By replacing the loss function with a carefully selected Sobolev norm, we can further amplify, dampen, counterbalance, or reverse the intrinsic frequency biasing in NN training. | https://openreview.net/pdf/e1dfef64f3cada7cf22d90626d7ddbdbb081e8d9.pdf |
Learning Multimodal Data Augmentation in Feature Space | https://openreview.net/forum?id=6SRDbbvU8s | https://openreview.net/forum?id=6SRDbbvU8s | Zichang Liu,Zhiqiang Tang,Xingjian Shi,Aston Zhang,Mu Li,Anshumali Shrivastava,Andrew Gordon Wilson | ICLR 2023,Poster | The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modality tasks like image classification. Indeed, it is particularly difficult to augment each modality while preserving the overall semantic structure of the data; for example, a caption may no longer be a good description of an image after standard augmentations have been applied, such as translation. Moreover, it is challenging to specify reasonable transformations that are not tailored to a particular modality. In this paper, we introduce LeMDA, Learning Multimodal Data Augmentation, an easy-to-use method that automatically learns to jointly augment multimodal data in feature space, with no constraints on the identities of the modalities or the relationship between modalities. We show that LeMDA can (1) profoundly improve the performance of multimodal deep learning architectures, (2) apply to combinations of modalities that have not been previously considered, and (3) achieve state-of-the-art results on a wide range of applications comprised of image, text, and tabular data. | https://openreview.net/pdf/32d27280c4c7b2da8c17dcd9e4ef4b4f16ec9733.pdf |
BigVGAN: A Universal Neural Vocoder with Large-Scale Training | https://openreview.net/forum?id=iTtGCMDEzS_ | https://openreview.net/forum?id=iTtGCMDEzS_ | Sang-gil Lee,Wei Ping,Boris Ginsburg,Bryan Catanzaro,Sungroh Yoon | ICLR 2023,Poster | Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. We introduce periodic activation function and anti-aliased representation into the GAN generator, which brings the desired inductive bias for audio synthesis and significantly improves audio quality. In addition, we train our GAN vocoder at the largest scale up to 112M parameters, which is unprecedented in the literature. We identify and address the failure modes in large-scale GAN training for audio, while maintaining high-fidelity output without over-regularization. Our BigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art performance for various zero-shot (out-of-distribution) conditions, including unseen speakers, languages, recording environments, singing voices, music, and instrumental audio. We release our code and model at: https://github.com/NVIDIA/BigVGAN | https://openreview.net/pdf/83a6bf43cf73a95dc165f0022b90a6950f10a5bf.pdf |
Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation | https://openreview.net/forum?id=zZhX4eYNeeh | https://openreview.net/forum?id=zZhX4eYNeeh | Arnob Ghosh,Xingyu Zhou,Ness Shroff | ICLR 2023,Poster | We study the infinite horizon average reward constrained Markov Decision Process (CMDP). In contrast to existing works on model-based, finite state space, we consider the model-free linear CMDP setup. We first propose a computationally inefficient algorithm and show that $\tilde{\mathcal{O}}(\sqrt{d^3T})$ regret and constraint violation can be achieved, in which $T$ is the number of interactions, and $d$ is the dimension of the feature mapping. We also propose an efficient variant based on the primal-dual adaptation of the LSVI-UCB algorithm and show that $\tilde{\mathcal{O}}((dT)^{3/4})$ regret and constraint violation can be achieved.
This improves the known regret bound of $\tilde{\mathcal{O}}(T^{5/6})$ for the finite state-space model-free constrained RL which was obtained under a stronger assumption compared to ours. We also develop an efficient policy-based algorithm via novel adaptation of the MDP-EXP2 algorithm to our primal-dual set up with $\tilde{\mathcal{O}}(\sqrt{T})$ regret and even zero constraint violation bound under a stronger set of assumptions. | https://openreview.net/pdf/1f7737b862cddaad3c73169398172437ec830f89.pdf |
Causal Imitation Learning via Inverse Reinforcement Learning | https://openreview.net/forum?id=B-z41MBL_tH | https://openreview.net/forum?id=B-z41MBL_tH | Kangrui Ruan,Junzhe Zhang,Xuan Di,Elias Bareinboim | ICLR 2023,Poster | One of the most common ways children learn when unfamiliar with the environment is by mimicking adults. Imitation learning concerns an imitator learning to behave in an unknown environment from an expert's demonstration; reward signals remain latent to the imitator. This paper studies imitation learning through causal lenses and extends the analysis and tools developed for behavior cloning (Zhang, Kumor, Bareinboim, 2020) to inverse reinforcement learning. First, we propose novel graphical conditions that allow the imitator to learn a policy performing as well as the expert's behavior policy, even when the imitator and the expert's state-action space disagree, and unobserved confounders (UCs) are present. When provided with parametric knowledge about the unknown reward function, such a policy may outperform the expert's. Also, our method is easily extensible and allows one to leverage existing IRL algorithms even when UCs are present, including the multiplicative-weights algorithm (MWAL) (Syed & Schapire, 2008) and the generative adversarial imitation learning (GAIL) (Ho & Ermon, 2016). Finally, we validate our framework by simulations using real-world and synthetic data. | https://openreview.net/pdf/2d0bee07a9f373073524d04d2dae5fab301d34be.pdf |
The Surprising Computational Power of Nondeterministic Stack RNNs | https://openreview.net/forum?id=o58JtGDs6y | https://openreview.net/forum?id=o58JtGDs6y | Brian DuSell,David Chiang | ICLR 2023,Poster | Traditional recurrent neural networks (RNNs) have a fixed, finite number of memory cells. In theory (assuming bounded range and precision), this limits their formal language recognition power to regular languages, and in practice, RNNs have been shown to be unable to learn many context-free languages (CFLs). In order to expand the class of languages RNNs recognize, prior work has augmented RNNs with a nondeterministic stack data structure, putting them on par with pushdown automata and increasing their language recognition power to CFLs. Nondeterminism is needed for recognizing all CFLs (not just deterministic CFLs), but in this paper, we show that nondeterminism and the neural controller interact to produce two more unexpected abilities. First, the nondeterministic stack RNN can recognize not only CFLs, but also many non-context-free languages. Second, it can recognize languages with much larger alphabet sizes than one might expect given the size of its stack alphabet. Finally, to increase the information capacity in the stack and allow it to solve more complicated tasks with large alphabet sizes, we propose a new version of the nondeterministic stack that simulates stacks of vectors rather than discrete symbols. We demonstrate perplexity improvements with this new model on the Penn Treebank language modeling benchmark. | https://openreview.net/pdf/2147456074506c0361c1c238697190296e801671.pdf |
Agnostic Learning of General ReLU Activation Using Gradient Descent | https://openreview.net/forum?id=EnrY5TOrbQ | https://openreview.net/forum?id=EnrY5TOrbQ | Pranjal Awasthi,Alex Tang,Aravindan Vijayaraghavan | ICLR 2023,Poster | We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions. Unlike prior work that studies the setting of zero bias, we consider the more challenging scenario when the bias of the ReLU function is non-zero. Our main result establishes that starting from random initialization, in a polynomial number of iterations gradient descent outputs, with high probability, a ReLU function that achieves an error that is within a constant factor of the optimal i.e., it is guaranteed to achieve an error of $O(OPT)$, where $OPT$ is the error of the best ReLU function. This is a significant improvement over existing guarantees for gradient descent, which only guarantee error of $O(\sqrt{d \cdot OPT})$ even in the zero-bias case (Frei et al., 2020). We also provide finite sample guarantees, and obtain similar guarantees for a broader class of marginal distributions beyond Gaussians. | https://openreview.net/pdf/b9d906693f00f15cfe7616ab7519953cb21895c4.pdf |
Learning Hyper Label Model for Programmatic Weak Supervision | https://openreview.net/forum?id=aCQt_BrkSjC | https://openreview.net/forum?id=aCQt_BrkSjC | Renzhi Wu,Shen-En Chen,Jieyu Zhang,Xu Chu | ICLR 2023,Poster | To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model to aggregate the output of multiple LFs to produce training labels. Most existing label models require a parameter learning step for each dataset. In this work, we present a hyper label model that (once learned) infers the ground-truth labels for each dataset in a single forward pass without dataset-specific parameter learning. The hyper label model approximates an optimal analytical (yet computationally intractable) solution of the ground-truth labels. We train the model on synthetic data generated in the way that ensures the model approximates the analytical optimal solution, and build the model upon Graph Neural Network (GNN) to ensure the model prediction being invariant (or equivariant) to the permutation of LFs (or data points). On 14 real-world datasets, our hyper label model outperforms the best existing methods in both accuracy (by 1.4 points on average) and efficiency (by six times on average). Our code is available at https://github.com/wurenzhi/hyper_label_model | https://openreview.net/pdf/1d4db3190f142ad660f267b6ac768334b52564af.pdf |
FedFA: Federated Feature Augmentation | https://openreview.net/forum?id=U9yFP90jU0 | https://openreview.net/forum?id=U9yFP90jU0 | Tianfei Zhou,Ender Konukoglu | ICLR 2023,Poster | Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among clients is naturally non-i.i.d., which leads to severe degradation of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm to address feature shift in clients’ samples, which can be caused by various factors, e.g., acquisition differences in medical imaging. To reach this goal, we propose FedFA to tackle federated learning from a dis- tinct perspective of federated feature augmentation. FedFA is based on a major insight that each client’s data distribution can be characterized by statistics (i.e., mean and standard deviation) of latent features; and it is likely to manipulate these local statistics globally, i.e., based on information in the entire federation, to let clients have a better sense of the underlying distribution and therefore alleviate local data bias. Based on this insight, we propose to augment each local feature statistic probabilistically based on a normal distribution, whose mean is the original statistic and variance quantifies the augmentation scope. Key to our approach is the determination of a meaningful Gaussian variance, which is accomplished by taking into account not only biased data of each individual client, but also underlying feature statistics characterized by all participating clients. We offer both theoretical and empirical justifications to verify the effectiveness of FedFA. Our code is available at https://github.com/tfzhou/FedFA.
| https://openreview.net/pdf/83d761ced643dbc84793236240410a72064dcf32.pdf |
Offline Congestion Games: How Feedback Type Affects Data Coverage Requirement | https://openreview.net/forum?id=PXVGer7hmJ | https://openreview.net/forum?id=PXVGer7hmJ | Haozhe Jiang,Qiwen Cui,Zhihan Xiong,Maryam Fazel,Simon Shaolei Du | ICLR 2023,Poster | This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games. The existing dataset coverage assumption in offline general-sum games inevitably incurs a dependency on the number of actions, which can be exponentially large in congestion games. We consider three different types of feedback with decreasing revealed information. Starting from the facility-level (a.k.a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and show a pessimism-type algorithm that can recover an approximate NE. For the agent-level (a.k.a., bandit) feedback setting, interestingly, we show the one-unit deviation coverage condition is not sufficient. On the other hand, we convert the game to multi-agent linear bandits and show that with a generalized data coverage assumption in offline linear bandits, we can efficiently recover the approximate NE. Lastly, we consider a novel type of feedback, the game-level feedback where only the total reward from all agents is revealed. Again, we show the coverage assumption for the agent-level feedback setting is insufficient in the game-level feedback setting, and with a stronger version of the data coverage assumption for linear bandits, we can recover an approximate NE. Together, our results constitute the first study of offline congestion games and imply formal separations between different types of feedback. | https://openreview.net/pdf/5d536b4991d001b1cb1a44b7b6d5785c2f3012d8.pdf |
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? | https://openreview.net/forum?id=2L9gzS80tA4 | https://openreview.net/forum?id=2L9gzS80tA4 | Lirui Wang,Kaiqing Zhang,Yunzhu Li,Yonglong Tian,Russ Tedrake | ICLR 2023,Poster | The success of machine learning relies heavily on massive amounts of data, which are usually generated and stored across a range of diverse and distributed data sources. Decentralized learning has thus been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortunately, the majority of real-world data are unlabeled and can be highly heterogeneous across sources. In this work, we carefully study decentralized learning with unlabeled data through the lens of self-supervised learning (SSL), specifically contrastive visual representation learning. We study the effectiveness of a range of contrastive learning algorithms under a decentralized learning setting, on relatively large-scale datasets including ImageNet-100, MS-COCO, and a new real-world robotic warehouse dataset. Our experiments show that the decentralized SSL (Dec-SSL) approach is robust to the heterogeneity of decentralized datasets, and learns useful representation for object classification, detection, and segmentation tasks, even when combined with the simple and standard decentralized learning algorithm of Federated Averaging (FedAvg). This robustness makes it possible to significantly reduce communication and to reduce the participation ratio of data sources with only minimal drops in performance. Interestingly, using the same amount of data, the representation learned by Dec-SSL can not only perform on par with that learned by centralized SSL which requires communication and excessive data storage costs, but also sometimes outperform representations extracted from decentralized SL which requires extra knowledge about the data labels. Finally, we provide theoretical insights into understanding why data heterogeneity is less of a concern for Dec-SSL objectives, and introduce feature alignment and clustering techniques to develop a new Dec-SSL algorithm that further improves the performance, in the face of highly non-IID data. Our study presents positive evidence to embrace unlabeled data in decentralized learning, and we hope to provide new insights into whether and why decentralized SSL is effective and/or even advantageous. | https://openreview.net/pdf/2eb5f4a351bec1c6159b8b5a9c19fde25dad2511.pdf |
Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds | https://openreview.net/forum?id=dQNL7Zsta3 | https://openreview.net/forum?id=dQNL7Zsta3 | Yoav Wald,Gal Yona,Uri Shalit,Yair Carmon | ICLR 2023,Poster | Learned classifiers should often possess certain invariance properties meant to encourage fairness, robustness, or out-of-distribution generalization.
However, multiple recent works empirically demonstrate that common invariance-inducing regularizers are ineffective in the over-parameterized regime, in which classifiers perfectly fit (i.e. interpolate) the training data. This suggests that the phenomenon of ``benign overfitting," in which models generalize well despite interpolating, might not favorably extend to settings in which robustness or fairness are desirable.
In this work, we provide a theoretical justification for these observations. We prove that---even in the simplest of settings---any interpolating learning rule (with an arbitrarily small margin) will not satisfy these invariance properties. We then propose and analyze an algorithm that---in the same setting---successfully learns a non-interpolating classifier that is provably invariant. We validate our theoretical observations on simulated data and the Waterbirds dataset. | https://openreview.net/pdf/47a6fc8f50c17a5f9230adfcbe67fccb2d5ccf2b.pdf |
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness | https://openreview.net/forum?id=aRTKuscKByJ | https://openreview.net/forum?id=aRTKuscKByJ | Yuancheng Xu,Yanchao Sun,Micah Goldblum,Tom Goldstein,Furong Huang | ICLR 2023,Poster | The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training. To understand this, we propose a continuous-time framework for quantifying the relative speed of the decision boundary with respect to each individual point. Through visualizing the moving speed of the decision boundary under Adversarial Training, one of the most effective robust training algorithms, a surprising moving-behavior is revealed: the decision boundary moves away from some vulnerable points but simultaneously moves closer to others, decreasing their margins. To alleviate these conflicting dynamics of the decision boundary, we propose Dynamics-aware Robust Training (DyART), which encourages the decision boundary to engage in movement that prioritizes increasing smaller margins. In contrast to prior works, DyART directly operates on the margins rather than their indirect approximations, allowing for more targeted and effective robustness improvement. Experiments on the CIFAR-10 and Tiny-ImageNet datasets verify that DyART alleviates the conflicting dynamics of the decision boundary and obtains improved robustness under various perturbation sizes compared to the state-of-the-art defenses. Our code is available at https://github.com/Yuancheng-Xu/Dynamics-Aware-Robust-Training. | https://openreview.net/pdf/6d36b2bf168f28c0392f579086eb7c72538d1db3.pdf |
SMART: Sentences as Basic Units for Text Evaluation | https://openreview.net/forum?id=OIe3kpwl40D | https://openreview.net/forum?id=OIe3kpwl40D | Reinald Kim Amplayo,Peter J Liu,Yao Zhao,Shashi Narayan | ICLR 2023,Poster | Widely used evaluation metrics for text generation either do not work well with longer texts or fail to evaluate all aspects of text quality. In this paper, we introduce a new metric called SMART to mitigate such limitations. Specifically, we treat sentences as basic units of matching instead of tokens, and use a sentence matching function to soft-match candidate and reference sentences. Candidate sentences are also compared to sentences in the source documents to allow grounding (e.g., factuality) evaluation. Our results show that system-level correlations of our proposed metric with a model-based matching function outperforms all competing metrics on the SummEval summarization meta-evaluation dataset, while the same metric with a string-based matching function is competitive with current model-based metrics. The latter does not use any neural model, which is useful during model development phases where resources can be limited and fast evaluation is required. SMART also outperforms all factuality evaluation metrics on the TRUE benchmark. Finally, we also conducted extensive analyses showing that our proposed metrics work well with longer summaries and are less biased towards specific models. | https://openreview.net/pdf/fa09a343ddf74204de08da6ac941674ebd4ca419.pdf |
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors | https://openreview.net/forum?id=SZdfz5k7cd1 | https://openreview.net/forum?id=SZdfz5k7cd1 | Zeyu Tang,Yatong Chen,Yang Liu,Kun Zhang | ICLR 2023,Poster | The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly. | https://openreview.net/pdf/a9d615296fdafac4c2274c026a6ba93a444debbc.pdf |
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections | https://openreview.net/forum?id=xYWqSjBcGMl | https://openreview.net/forum?id=xYWqSjBcGMl | Edward De Brouwer,Rahul G Krishnan | ICLR 2023,Poster | Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update the latent representation of the dynamical system. Existing parameterizations for the dynamics functions of Neural ODEs limit the ability of the model to retain global information about the time series; specifically, a piece-wise integration of the latent process between observations can result in a loss of memory on the dynamic patterns of previously observed data points. We propose PolyODE, a Neural ODE that models the latent continuous-time process as a projection onto a basis of orthogonal polynomials. This formulation enforces long-range memory and preserves a global representation of the underlying dynamical system. Our construction is backed by favourable theoretical guarantees and in a series of experiments, we demonstrate that it outperforms previous works in the reconstruction of past and future data, and in downstream prediction tasks. | https://openreview.net/pdf/1e81b0e867b9b57649971121b1b5e3536d062458.pdf |
AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks | https://openreview.net/forum?id=y81ppNf_vg | https://openreview.net/forum?id=y81ppNf_vg | Kaidi Cao,Jiaxuan You,Jiaju Liu,Jure Leskovec | ICLR 2023,Poster | AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. However, most present AutoML techniques consider each task independently from scratch, which requires exploring many architectures, leading to high computational cost. Here we propose AutoTransfer, an AutoML solution that improves search efficiency by transferring the prior architectural design knowledge to the novel task of interest. Our key innovation includes a task-model bank that captures the model performance over a diverse set of GNN architectures and tasks, and a computationally efficient task embedding that can accurately measure the similarity among different tasks. Based on the task-model bank and the task embeddings, we estimate the design priors of desirable models of the novel task, by aggregating a similarity-weighted sum of the top-K design distributions on tasks that are similar to the task of interest. The computed design priors can be used with any AutoML search algorithm. We evaluate AutoTransfer on six datasets in the graph machine learning domain. Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude. Finally, we release GNN-Bank-101, a large-scale dataset of detailed GNN training information of 120,000 task-model combinations to facilitate and inspire future research. | https://openreview.net/pdf/6330ac58c19cf1463f00677c0241f69acdd44741.pdf |
Temporal Dependencies in Feature Importance for Time Series Prediction | https://openreview.net/forum?id=C0q9oBc3n4 | https://openreview.net/forum?id=C0q9oBc3n4 | Kin Kwan Leung,Clayton Rooke,Jonathan Smith,Saba Zuberi,Maksims Volkovs | ICLR 2023,Poster | Time series data introduces two key challenges for explainability methods: firstly, observations of the same feature over subsequent time steps are not independent, and secondly, the same feature can have varying importance to model predictions over time. In this paper, we propose Windowed Feature Importance in Time (WinIT), a feature removal based explainability approach to address these issues. Unlike existing feature removal explanation methods, WinIT explicitly accounts for the temporal dependence between different observations of the same feature in the construction of its importance score. Furthermore, WinIT captures the varying importance of a feature over time, by summarizing its importance over a window of past time steps. We conduct an extensive empirical study on synthetic and real-world data, compare against a wide range of leading explainability methods, and explore the impact of various evaluation strategies. Our results show that WinIT achieves significant gains over existing methods, with more consistent performance across different evaluation metrics. | https://openreview.net/pdf/0f72b3a91d343251112a9846581eefec130b00b6.pdf |
Characterizing the spectrum of the NTK via a power series expansion | https://openreview.net/forum?id=Tvms8xrZHyR | https://openreview.net/forum?id=Tvms8xrZHyR | Michael Murray,Hui Jin,Benjamin Bowman,Guido Montufar | ICLR 2023,Poster | Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the network. We observe faster decay of the Hermite coefficients leads to faster decay in the NTK coefficients and explore the role of depth. Using this series, first we relate the effective rank of the NTK to the effective rank of the input-data Gram. Second, for data drawn uniformly on the sphere we study the eigenvalues of the NTK, analyzing the impact of the choice of activation function. Finally, for generic data and activation functions with sufficiently fast Hermite coefficient decay, we derive an asymptotic upper bound on the spectrum of the NTK. | https://openreview.net/pdf/d4af9cb9e578f203d2f33787c8bb73e011e4b9b2.pdf |
A critical look at the evaluation of GNNs under heterophily: Are we really making progress? | https://openreview.net/forum?id=tJbbQfw-5wv | https://openreview.net/forum?id=tJbbQfw-5wv | Oleg Platonov,Denis Kuznedelev,Michael Diskin,Artem Babenko,Liudmila Prokhorenkova | ICLR 2023,Poster | Node classification is a classical graph representation learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that standard GNNs only work well for homophilous graphs, i.e., graphs where edges tend to connect nodes of the same class. Graphs without this property are called heterophilous, and it is typically assumed that specialized methods are required to achieve strong performance on such graphs. In this work, we challenge this assumption. First, we show that the standard datasets used for evaluating heterophily-specific models have serious drawbacks, making results obtained by using them unreliable. The most significant of these drawbacks is the presence of a large number of duplicate nodes in the datasets Squirrel and Chameleon, which leads to train-test data leakage. We show that removing duplicate nodes strongly affects GNN performance on these datasets. Then, we propose a set of heterophilous graphs of varying properties that we believe can serve as a better benchmark for evaluating the performance of GNNs under heterophily. We show that standard GNNs achieve strong results on these heterophilous graphs, almost always outperforming specialized models. Our datasets and the code for reproducing our experiments are available at https://github.com/yandex-research/heterophilous-graphs | https://openreview.net/pdf/d721a44b48bd9e0406aeb2cdcb2467e0c27748b9.pdf |
A Non-monotonic Self-terminating Language Model | https://openreview.net/forum?id=vw-5EgYbJZr | https://openreview.net/forum?id=vw-5EgYbJZr | Eugene Choi,Kyunghyun Cho,Cheolhyoung Lee | ICLR 2023,Poster | Recent large-scale neural autoregressive sequence models have shown impressive performances on a variety of natural language generation tasks. However, their generated sequences often exhibit degenerate properties such as non-termination, undesirable repetition, and premature termination, when generated with decoding algorithms such as greedy search, beam search, top-$k$ sampling, and nucleus sampling. In this paper, we focus on the problem of non-terminating sequences resulting from an incomplete decoding algorithm. We first define an incomplete probable decoding algorithm which includes greedy search, top-$k$ sampling, and nucleus sampling, beyond the incomplete decoding algorithm originally put forward by Welleck et al. (2020). We then propose a non-monotonic self-terminating language model, which significantly relaxes the constraint of monotonically increasing termination probability in the originally proposed self-terminating language model by Welleck et al. (2020), to address the issue of non-terminating sequences when using incomplete probable decoding algorithms. We prove that our proposed model prevents non-terminating sequences when using not only incomplete probable decoding algorithms but also beam search. We empirically validate our model on sequence completion tasks with various architectures. | https://openreview.net/pdf/5af17fbafcba261932381990540df8840ffec3f0.pdf |
Learning to Segment from Noisy Annotations: A Spatial Correction Approach | https://openreview.net/forum?id=Qc_OopMEBnC | https://openreview.net/forum?id=Qc_OopMEBnC | Jiachen Yao,Yikai Zhang,Songzhu Zheng,Mayank Goswami,Prateek Prasanna,Chao Chen | ICLR 2023,Poster | Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly tackle label noise in classification tasks. Their independent-noise assumptions do not fit label noise in segmentation task. In this paper, we propose a novel noise model for segmentation problems that encodes spatial correlation and bias, which are prominent in segmentation annotations. Further, to mitigate such label noise, we propose a label correction method to recover true label progressively. We provide theoretical guarantees of the correctness of the proposed method. Experiments show that our approach outperforms current state-of-the-art methods on both synthetic and real-world noisy annotations. | https://openreview.net/pdf/3a19f70c903f3d5336ce11a5aa6ac1f5f12152af.pdf |
Measuring Forgetting of Memorized Training Examples | https://openreview.net/forum?id=7bJizxLKrR | https://openreview.net/forum?id=7bJizxLKrR | Matthew Jagielski,Om Thakkar,Florian Tramer,Daphne Ippolito,Katherine Lee,Nicholas Carlini,Eric Wallace,Shuang Song,Abhradeep Guha Thakurta,Nicolas Papernot,Chiyuan Zhang | ICLR 2023,Poster | Machine learning models exhibit two seemingly contradictory phenomena: training data memorization and various forms of forgetting. In memorization, models overfit specific training examples and become susceptible to privacy attacks. In forgetting, examples which appeared early in training are forgotten by the end. In this work, we connect these phenomena.
We propose a technique to measure to what extent models ``forget'' the specifics of training examples, becoming less susceptible to privacy attacks on examples they have not seen recently.
We show that, while non-convexity can prevent forgetting from happening in the worst-case, standard image,speech, and language models empirically do forget examples over time.
We identify nondeterminism as a potential explanation, showing that deterministically trained models do not forget.
Our results suggest that examples seen early when training with extremely large datasets---for instance those examples used to pre-train a model---may observe privacy benefits at the expense of examples seen later. | https://openreview.net/pdf/b47ef8e9b1dd21b0fae0c4bde47a71e27324e333.pdf |
MaskViT: Masked Visual Pre-Training for Video Prediction | https://openreview.net/forum?id=QAV2CcLEDh | https://openreview.net/forum?id=QAV2CcLEDh | Agrim Gupta,Stephen Tian,Yunzhi Zhang,Jiajun Wu,Roberto Martín-Martín,Li Fei-Fei | ICLR 2023,Poster | The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our approach, named MaskViT, is based on two simple design decisions. First, for memory and training efficiency, we use two types of window attention: spatial and spatiotemporal. Second, during training, we mask a variable percentage of tokens instead of a fixed mask ratio. For inference, MaskViT generates all tokens via iterative refinement where we incrementally decrease the masking ratio following a mask scheduling function. On several datasets we demonstrate that MaskViT outperforms prior works in video prediction, is parameter efficient, and can generate high resolution videos ($256 \times $256). Further, we demonstrate the benefits of inference speedup (up to $512 \times$) due to iterative decoding by using MaskViT for planning on a real robot. Our work suggests that we can endow embodied agents with powerful predictive models by leveraging the general framework of masked visual modeling with minimal domain knowledge. | https://openreview.net/pdf/519ede840ab88f1eeeef20446b915f73429dec70.pdf |
Text Summarization with Oracle Expectation | https://openreview.net/forum?id=HehQobsr0S | https://openreview.net/forum?id=HehQobsr0S | Yumo Xu,Mirella Lapata | ICLR 2023,Poster | Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Since most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy, different labeling algorithms have been proposed to extrapolate oracle extracts for model training. In this work, we identify two flaws with the widely used greedy labeling approach: it delivers suboptimal and deterministic oracles. To alleviate both issues, we propose a simple yet effective labeling algorithm that creates soft, expectation-based sentence labels. We define a new learning objective for extractive summarization which incorporates learning signals from multiple oracle summaries and prove it is equivalent to estimating the oracle expectation for each document sentence. Without any architectural modifications, the proposed labeling scheme achieves superior performance on a variety of summarization benchmarks across domains and languages, in both supervised and zero-shot settings. | https://openreview.net/pdf/5b574baaf992ad0f3f1dae8da1bb5508b219aec8.pdf |
Continuous-time identification of dynamic state-space models by deep subspace encoding | https://openreview.net/forum?id=_4n3k3d1ob | https://openreview.net/forum?id=_4n3k3d1ob | Gerben I. Beintema,Maarten Schoukens,Roland Tóth | ICLR 2023,Poster | Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. | https://openreview.net/pdf/b05187a11aa24e2d51a41d36107f572dd2700a09.pdf |
How to Train your HIPPO: State Space Models with Generalized Orthogonal Basis Projections | https://openreview.net/forum?id=klK17OQ3KB | https://openreview.net/forum?id=klK17OQ3KB | Albert Gu,Isys Johnson,Aman Timalsina,Atri Rudra,Christopher Re | ICLR 2023,Poster | Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4). A core component of S4 involves initializing the SSM state matrix to a particular matrix called a HiPPO matrix, which was empirically important for S4's ability to handle long sequences. However, the specific matrix that S4 uses was actually derived in previous work for a particular *time-varying* dynamical system, and the use of this matrix as a *time-invariant* SSM had no known mathematical interpretation. Consequently, the theoretical mechanism by which S4 models long-range dependencies actually remains unexplained. We derive a more general and intuitive formulation of the HiPPO framework, which provides a simple mathematical interpretation of S4 as a decomposition onto exponentially-warped Legendre polynomials, explaining its ability to capture long dependencies. Our generalization introduces a theoretically rich class of SSMs that also lets us derive more intuitive S4 variants for other bases such as the Fourier basis, and explains other aspects of training S4, such as how to initialize the important timescale parameter. These insights improve S4's performance to 86% on the Long Range Arena benchmark, with 96% on the most difficult Path-X task.
| https://openreview.net/pdf/e17a18bf260fa995706df28fd2c25ffe40993a04.pdf |
Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization | https://openreview.net/forum?id=TkQ1sxd9P4 | https://openreview.net/forum?id=TkQ1sxd9P4 | Prince Osei Aboagye,Yan Zheng,Jack Shunn,Chin-Chia Michael Yeh,Junpeng Wang,Zhongfang Zhuang,Huiyuan Chen,Liang Wang,Wei Zhang,Jeff Phillips | ICLR 2023,Poster | We propose a new mechanism to augment a word vector embedding representation that offers improved bias removal while retaining the key information—resulting in improved interpretability of the representation. Rather than removing the information associated with a concept that may induce bias, our proposed method identifies two concept subspaces and makes them orthogonal. The resulting representation has these two concepts uncorrelated. Moreover, because they are orthogonal, one can simply apply a rotation on the basis of the representation so that the resulting subspace corresponds with coordinates. This explicit encoding of concepts to coordinates works because they have been made fully orthogonal, which previous approaches do not achieve. Furthermore, we show that this can be extended to multiple subspaces. As a result, one can choose a subset of concepts to be represented transparently and explicitly, while the others are retained in the mixed but extremely expressive format of the representation. | https://openreview.net/pdf/d26c27d5b0cd033934902b0316e1e04ec0fb0ec1.pdf |
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations | https://openreview.net/forum?id=jwdqNwyREyh | https://openreview.net/forum?id=jwdqNwyREyh | Ziyu Jiang,Yinpeng Chen,Mengchen Liu,Dongdong Chen,Xiyang Dai,Lu Yuan,Zicheng Liu,Zhangyang Wang | ICLR 2023,Poster | Recently, both Contrastive Learning (CL) and Mask Image Modeling (MIM) demonstrate that self-supervision is powerful to learn good representations. However, naively combining them is far from success. In this paper, we start by making the empirical observation that a naive joint optimization of CL and MIM losses leads to conflicting gradient directions - more severe as the layers go deeper. This motivates us to shift the paradigm from combining loss at the end, to choosing the proper learning method per network layer. Inspired by experimental observations, we find that MIM and CL are suitable to lower and higher layers, respectively. We hence propose to combine them in a surprisingly simple, ``sequential cascade'' fashion: early layers are first trained under one MIM loss, on top of which latter layers continue to be trained under another CL loss. The proposed Layer Grafted Pre-training learns good visual representations that demonstrate superior label efficiency in downstream applications, in particular yielding strong few-shot performance besides linear evaluation. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. The code is available at https://github.com/VITA-Group/layerGraftedPretraining_ICLR23.git.
| https://openreview.net/pdf/1d8972d5afc36b1f7508cd08a231b9a41347530b.pdf |
Discovering Latent Knowledge in Language Models Without Supervision | https://openreview.net/forum?id=ETKGuby0hcs | https://openreview.net/forum?id=ETKGuby0hcs | Collin Burns,Haotian Ye,Dan Klein,Jacob Steinhardt | ICLR 2023,Poster | Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels. | https://openreview.net/pdf/1a2f757c6a56314972af9e4b40e580d5691e0b55.pdf |
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation | https://openreview.net/forum?id=H0gdPxSwkPb | https://openreview.net/forum?id=H0gdPxSwkPb | Boah Kim,Yujin Oh,Jong Chul Ye | ICLR 2023,Poster | Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of ground-truth labels are required in supervised methods and confusing background structures make neural networks hard to segment vessels in an unsupervised manner. To address this, here we introduce a novel diffusion adversarial representation learning (DARL) model that leverages a denoising diffusion probabilistic model with adversarial learning, and apply it to vessel segmentation. In particular, for self-supervised vessel segmentation, DARL learns the background signal using a diffusion module, which lets a generation module effectively provide vessel representations. Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information. Once the proposed model is trained, the model generates segmentation masks in a single step and can be applied to general vascular structure segmentation of coronary angiography and retinal images. Experimental results on various datasets show that our method significantly outperforms existing unsupervised and self-supervised vessel segmentation methods. | https://openreview.net/pdf/daadbc20b5665f981c5c27eeb20cb5e9ba6ac96e.pdf |
Noise Injection Node Regularization for Robust Learning | https://openreview.net/forum?id=gmSZ-GPNY6 | https://openreview.net/forum?id=gmSZ-GPNY6 | Noam Itzhak Levi,Itay Mimouni Bloch,Marat Freytsis,Tomer Volansky | ICLR 2023,Poster | We introduce Noise Injection Node Regularization (NINR), a method of injecting structured noise into Deep Neural Networks (DNN) during the training stage, resulting in an emergent regularizing effect. We present theoretical and empirical evidence for substantial improvement in robustness against various test data perturbations for feed-forward DNNs when trained under NINR. The novelty in our approach comes from the interplay of adaptive noise injection and initialization conditions such that noise is the dominant driver of dynamics at the start of training. As it simply requires the addition of external nodes without altering the existing network structure or optimization algorithms, this method can be easily incorporated into many standard problem specifications. We find improved stability against a number of data perturbations, including domain shifts, with the most dramatic improvement obtained for unstructured noise, where our technique outperforms other existing methods such as dropout or $L_2$ regularization, in some cases. We further show that desirable generalization properties on clean data are generally maintained. | https://openreview.net/pdf/4c91ec48b89ce6f6d8504f69ae9036330c105901.pdf |
Efficient Edge Inference by Selective Query | https://openreview.net/forum?id=jpR98ZdIm2q | https://openreview.net/forum?id=jpR98ZdIm2q | Anil Kag,Igor Fedorov,Aditya Gangrade,Paul Whatmough,Venkatesh Saligrama | ICLR 2023,Poster | Edge devices provide inference on predictive tasks to many end-users. However, deploying deep neural networks that achieve state-of-the-art accuracy on these devices is infeasible due to edge resource constraints. Nevertheless, cloud-only processing, the de-facto standard, is also problematic, since uploading large amounts of data imposes severe communication bottlenecks. We propose a novel end-to-end hybrid learning framework that allows the edge to selectively query only those hard examples that the cloud can classify correctly. Our framework optimizes over neural architectures and trains edge predictors and routing models so that the overall accuracy remains high while minimizing the overall latency. Training a hybrid learner is difficult since we lack annotations of hard edge-examples. We introduce a novel proxy supervision in this context and show that our method adapts seamlessly and near optimally across different latency regimes. On the ImageNet dataset, our proposed method deployed on a micro-controller unit exhibits $25\%$ reduction in latency compared to cloud-only processing while suffering no excess loss. | https://openreview.net/pdf/ea28d8d8d31fb85d39fdee73987653a2a5ecabc6.pdf |
Human-level Atari 200x faster | https://openreview.net/forum?id=JtC6yOHRoJJ | https://openreview.net/forum?id=JtC6yOHRoJJ | Steven Kapturowski,Víctor Campos,Ray Jiang,Nemanja Rakicevic,Hado van Hasselt,Charles Blundell,Adria Puigdomenech Badia | ICLR 2023,Poster | The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subject of research of a large body of work, with performance frequently measured by observing scores over the wide range of environments contained in the Atari 57 benchmark. Agent57 was the first agent to surpass the human benchmark on all 57 games, but this came at the cost of poor data-efficiency, requiring nearly 80 billion frames of experience to achieve. Taking Agent57 as a starting point, we employ a diverse set of strategies to achieve a 200-fold reduction of experience needed to outperform the human baseline, within our novel agent MEME. We investigate a range of instabilities and bottlenecks we encountered while reducing the data regime, and propose effective solutions to build a more robust and efficient agent. We also demonstrate competitive performance with high-performing methods such as Muesli and MuZero. Our contributions aim to achieve faster propagation of learning signals related to rare events, stabilize learning under differing value scales, improve the neural network architecture, and make updates more robust under a rapidly-changing policy. | https://openreview.net/pdf/b23bc123e103d66e46f6b7516e3fb6dffd1d2cba.pdf |
Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks | https://openreview.net/forum?id=mkJm5Uy4HrQ | https://openreview.net/forum?id=mkJm5Uy4HrQ | Charles Jin,Melinda Sun,Martin Rinard | ICLR 2023,Poster | We propose a novel clustering mechanism based on an incompatibility property between subsets of data that emerges during model training. This mechanism partitions the dataset into subsets that generalize only to themselves, i.e., training on one subset does not improve performance on the other subsets. Leveraging the interaction between the dataset and the training process, our clustering mechanism partitions datasets into clusters that are defined by—and therefore meaningful to—the objective of the training process.
We apply our clustering mechanism to defend against data poisoning attacks, in which the attacker injects malicious poisoned data into the training dataset to affect the trained model's output. Our evaluation focuses on backdoor attacks against deep neural networks trained to perform image classification using the GTSRB and CIFAR-10 datasets. Our results show that (1) these attacks produce poisoned datasets in which the poisoned and clean data are incompatible and (2) our technique successfully identifies (and removes) the poisoned data. In an end-to-end evaluation, our defense reduces the attack success rate to below 1% on 134 out of 165 scenarios, with only a 2% drop in clean accuracy on CIFAR-10 and a negligible drop in clean accuracy on GTSRB. | https://openreview.net/pdf/e27bb4c7787b3770053151428e69c5ab0f279dd2.pdf |
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games | https://openreview.net/forum?id=DpE5UYUQzZH | https://openreview.net/forum?id=DpE5UYUQzZH | Samuel Sokota,Ryan D'Orazio,J Zico Kolter,Nicolas Loizou,Marc Lanctot,Ioannis Mitliagkas,Noam Brown,Christian Kroer | ICLR 2023,Poster | This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player zero-sum games. These virtues include: 1) Being the first quantal response equilibria solver to achieve linear convergence for extensive-form games with first order feedback; 2) Being the first standard reinforcement learning algorithm to achieve empirically competitive results with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning algorithm. | https://openreview.net/pdf/81f8c15b95a73eae01692db8cf41977e55d1d108.pdf |
Pitfalls of Gaussians as a noise distribution in NCE | https://openreview.net/forum?id=ovZE0KsbM3S | https://openreview.net/forum?id=ovZE0KsbM3S | Holden Lee,Chirag Pabbaraju,Anish Prasad Sevekari,Andrej Risteski | ICLR 2023,Poster | Noise Contrastive Estimation (NCE) is a popular approach for learning probability density functions parameterized up to a constant of proportionality. The main idea is to design a classification problem for distinguishing training data from samples from an (easy-to-sample) noise distribution $q$, in a manner that avoids having to calculate a partition function. It is well-known that the choice of $q$ can severely impact the computational and statistical efficiency of NCE. In practice, a common choice for $q$ is a Gaussian which matches the mean and covariance of the data.
In this paper, we show that such a choice can result in an exponentially bad (in the ambient dimension) conditioning of the Hessian of the loss - even for very simple data distributions. As a consequence, both the statistical and algorithmic complexity for such a choice of $q$ will be problematic in practice - suggesting that more complex noise distributions are essential to the success of NCE. | https://openreview.net/pdf/c2e7c91ab54004c63f32b30e65bb5847fee00037.pdf |
Scaling Laws for a Multi-Agent Reinforcement Learning Model | https://openreview.net/forum?id=ZrEbzL9eQ3W | https://openreview.net/forum?id=ZrEbzL9eQ3W | Oren Neumann,Claudius Gros | ICLR 2023,Poster | The recent observation of neural power-law scaling relations has made a significant impact in the field of deep learning. A substantial amount of attention has been dedicated as a consequence to the description of scaling laws, although mostly for supervised learning and only to a reduced extent for reinforcement learning frameworks. In this paper we present an extensive study of performance scaling for a cornerstone reinforcement learning algorithm, AlphaZero. On the basis of a relationship between Elo rating, playing strength and power-law scaling, we train AlphaZero agents on the games Connect Four and Pentago and analyze their performance. We find that player strength scales as a power law in neural network parameter count when not bottlenecked by available compute, and as a power of compute when training optimally sized agents. We observe nearly identical scaling exponents for both games. Combining the two observed scaling laws we obtain a power law relating optimal size to compute similar to the ones observed for language models. We find that the predicted scaling of optimal neural network size fits our data for both games. This scaling law implies that previously published state-of-the-art game-playing models are significantly smaller than their optimal size, given the respective compute budgets. We also show that large AlphaZero models are more sample efficient, performing better than smaller models with the same amount of training data. | https://openreview.net/pdf/f98ab0085243b503d56efc920c90c19b95dc7ec8.pdf |
Perfectly Secure Steganography Using Minimum Entropy Coupling | https://openreview.net/forum?id=HQ67mj5rJdR | https://openreview.net/forum?id=HQ67mj5rJdR | Christian Schroeder de Witt,Samuel Sokota,J Zico Kolter,Jakob Nicolaus Foerster,Martin Strohmeier | ICLR 2023,Poster | Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques. In this work, we show that a steganography procedure is perfectly secure under Cachin (1998)'s information theoretic-model of steganography if and only if it is induced by a coupling. Furthermore, we show that, among perfectly secure procedures, a procedure is maximally efficient if and only if it is induced by a minimum entropy coupling. These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees with non-trivial efficiency; additionally, these algorithms are highly scalable. To provide empirical validation, we compare a minimum entropy coupling-based approach to three modern baselines---arithmetic coding, Meteor, and adaptive dynamic grouping---using GPT-2, WaveRNN, and Image Transformer as communication channels. We find that the minimum entropy coupling-based approach achieves superior encoding efficiency, despite its stronger security constraints. In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling. | https://openreview.net/pdf/352b361ac5a6926c6bbf58158ea34332012567a4.pdf |
Calibrating Transformers via Sparse Gaussian Processes | https://openreview.net/forum?id=jPVAFXHlbL | https://openreview.net/forum?id=jPVAFXHlbL | Wenlong Chen,Yingzhen Li | ICLR 2023,Poster | Transformer models have achieved profound success in prediction tasks in a wide range of applications in natural language processing, speech recognition and computer vision. Extending Transformer’s success to safety-critical domains requires calibrated uncertainty estimation which remains under-explored. To address this, we propose Sparse Gaussian Process attention (SGPA), which performs Bayesian inference directly in the output space of multi-head attention blocks (MHAs) in transformer to calibrate its uncertainty. It replaces the scaled dot-product operation with a valid symmetric kernel and uses sparse Gaussian processes (SGP) techniques to approximate the posterior processes of MHA outputs. Empirically, on a suite of prediction tasks on text, images and graphs, SGPA-based Transformers achieve competitive predictive accuracy, while noticeably improving both in-distribution calibration and out-of-distribution robustness and detection. | https://openreview.net/pdf/4e1dd410fa8dc35819ebe4426df19017fbedbb7f.pdf |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.