title stringlengths 10 192 | authors stringlengths 7 342 | abstract stringlengths 82 4.51k | url stringlengths 44 59 | detail_url stringlengths 44 59 | abs stringlengths 44 59 | OpenReview stringclasses 1 value | Download PDF stringlengths 47 77 | tags stringclasses 1 value |
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Lower-Bounded Proper Losses for Weakly Supervised Classification | Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama | This paper discusses the problem of weakly supervised classification, in which instances are given weak labels that are produced by some label-corruption process. The goal is to derive conditions under which loss functions for weak-label learning are proper and lower-bounded—two essential requirements for the losses used in class-probability estimation. To this end, we derive a representation theorem for proper losses in supervised learning, which dualizes the Savage representation. We use this theorem to characterize proper weak-label losses and find a condition for them to be lower-bounded. From these theoretical findings, we derive a novel regularization scheme called generalized logit squeezing, which makes any proper weak-label loss bounded from below, without losing properness. Furthermore, we experimentally demonstrate the effectiveness of our proposed approach, as compared to improper or unbounded losses. The results highlight the importance of properness and lower-boundedness. | https://proceedings.mlr.press/v139/yoshida21a.html | https://proceedings.mlr.press/v139/yoshida21a.html | https://proceedings.mlr.press/v139/yoshida21a.html | http://proceedings.mlr.press/v139/yoshida21a/yoshida21a.pdf | ICML 2021 | |
Graph Contrastive Learning Automated | Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang | Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, graph contrastive learning (GraphCL) has emerged with promising representation learning performance. Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data. That significantly limits the more general applicability of GraphCL. Aiming to fill in this crucial gap, this paper proposes a unified bi-level optimization framework to automatically, adaptively and dynamically select data augmentations when performing GraphCL on specific graph data. The general framework, dubbed JOint Augmentation Optimization (JOAO), is instantiated as min-max optimization. The selections of augmentations made by JOAO are shown to be in general aligned with previous "best practices" observed from handcrafted tuning: yet now being automated, more flexible and versatile. Moreover, we propose a new augmentation-aware projection head mechanism, which will route output features through different projection heads corresponding to different augmentations chosen at each training step. Extensive experiments demonstrate that JOAO performs on par with or sometimes better than the state-of-the-art competitors including GraphCL, on multiple graph datasets of various scales and types, yet without resorting to any laborious dataset-specific tuning on augmentation selection. We release the code at https://github.com/Shen-Lab/GraphCL_Automated. | https://proceedings.mlr.press/v139/you21a.html | https://proceedings.mlr.press/v139/you21a.html | https://proceedings.mlr.press/v139/you21a.html | http://proceedings.mlr.press/v139/you21a/you21a.pdf | ICML 2021 | |
LogME: Practical Assessment of Pre-trained Models for Transfer Learning | Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long | This paper studies task adaptive pre-trained model selection, an underexplored problem of assessing pre-trained models for the target task and select best ones from the model zoo \emph{without fine-tuning}. A few pilot works addressed the problem in transferring supervised pre-trained models to classification tasks, but they cannot handle emerging unsupervised pre-trained models or regression tasks. In pursuit of a practical assessment method, we propose to estimate the maximum value of label evidence given features extracted by pre-trained models. Unlike the maximum likelihood, the maximum evidence is \emph{immune to over-fitting}, while its expensive computation can be dramatically reduced by our carefully designed algorithm. The Logarithm of Maximum Evidence (LogME) can be used to assess pre-trained models for transfer learning: a pre-trained model with a high LogME value is likely to have good transfer performance. LogME is \emph{fast, accurate, and general}, characterizing itself as the first practical method for assessing pre-trained models. Compared with brute-force fine-tuning, LogME brings at most $3000\times$ speedup in wall-clock time and requires only $1%$ memory footprint. It outperforms prior methods by a large margin in their setting and is applicable to new settings. It is general enough for diverse pre-trained models (supervised pre-trained and unsupervised pre-trained), downstream tasks (classification and regression), and modalities (vision and language). Code is available at this repository: \href{https://github.com/thuml/LogME}{https://github.com/thuml/LogME}. | https://proceedings.mlr.press/v139/you21b.html | https://proceedings.mlr.press/v139/you21b.html | https://proceedings.mlr.press/v139/you21b.html | http://proceedings.mlr.press/v139/you21b/you21b.pdf | ICML 2021 | |
Exponentially Many Local Minima in Quantum Neural Networks | Xuchen You, Xiaodi Wu | Quantum Neural Networks (QNNs), or the so-called variational quantum circuits, are important quantum applications both because of their similar promises as classical neural networks and because of the feasibility of their implementation on near-term intermediate-size noisy quantum machines (NISQ). However, the training task of QNNs is challenging and much less understood. We conduct a quantitative investigation on the landscape of loss functions of QNNs and identify a class of simple yet extremely hard QNN instances for training. Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters. Moreover, we show the optimality of our construction by providing an almost matching upper bound on such dependence. While local minima in classical neural networks are due to non-linear activations, in quantum neural networks local minima appear as a result of the quantum interference phenomenon. Finally, we empirically confirm that our constructions can indeed be hard instances in practice with typical gradient-based optimizers, which demonstrates the practical value of our findings. | https://proceedings.mlr.press/v139/you21c.html | https://proceedings.mlr.press/v139/you21c.html | https://proceedings.mlr.press/v139/you21c.html | http://proceedings.mlr.press/v139/you21c/you21c.pdf | ICML 2021 | |
DAGs with No Curl: An Efficient DAG Structure Learning Approach | Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji | Recently directed acyclic graph (DAG) structure learning is formulated as a constrained continuous optimization problem with continuous acyclicity constraints and was solved iteratively through subproblem optimization. To further improve efficiency, we propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly. Specifically, we first show that the set of weighted adjacency matrices of DAGs are equivalent to the set of weighted gradients of graph potential functions, and one may perform structure learning by searching in this equivalent set of DAGs. To instantiate this idea, we propose a new algorithm, DAG-NoCurl, which solves the optimization problem efficiently with a two-step procedure: $1)$ first we find an initial non-acyclic solution to the optimization problem, and $2)$ then we employ the Hodge decomposition of graphs and learn an acyclic graph by projecting the non-acyclic graph to the gradient of a potential function. Experimental studies on benchmark datasets demonstrate that our method provides comparable accuracy but better efficiency than baseline DAG structure learning methods on both linear and generalized structural equation models, often by more than one order of magnitude. | https://proceedings.mlr.press/v139/yu21a.html | https://proceedings.mlr.press/v139/yu21a.html | https://proceedings.mlr.press/v139/yu21a.html | http://proceedings.mlr.press/v139/yu21a/yu21a.pdf | ICML 2021 | |
Provably Efficient Algorithms for Multi-Objective Competitive RL | Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra | We study multi-objective reinforcement learning (RL) where an agent’s reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average return vector to a target set. We develop statistically and computationally efficient algorithms to approach the associated target set. Our results extend Blackwell’s approachability theorem \citep{blackwell1956analog} to tabular RL, where strategic exploration becomes essential. The algorithms presented are adaptive; their guarantees hold even without Blackwell’s approachability condition. If the opponents use fixed policies, we give an improved rate of approaching the target set while also tackling the more ambitious goal of simultaneously minimizing a scalar cost function. We discuss our analysis for this special case by relating our results to previous works on constrained RL. To our knowledge, this work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal. | https://proceedings.mlr.press/v139/yu21b.html | https://proceedings.mlr.press/v139/yu21b.html | https://proceedings.mlr.press/v139/yu21b.html | http://proceedings.mlr.press/v139/yu21b/yu21b.pdf | ICML 2021 | |
Whittle Networks: A Deep Likelihood Model for Time Series | Zhongjie Yu, Fabrizio G Ventola, Kristian Kersting | While probabilistic circuits have been extensively explored for tabular data, less attention has been paid to time series. Here, the goal is to estimate joint densities among the entire time series and, in turn, determining, for instance, conditional independence relations between them. To this end, we propose the first probabilistic circuits (PCs) approach for modeling the joint distribution of multivariate time series, called Whittle sum-product networks (WSPNs). WSPNs leverage the Whittle approximation, casting the likelihood in the frequency domain, and place a complex-valued sum-product network, the most prominent PC, over the frequencies. The conditional independence relations among the time series can then be determined efficiently in the spectral domain. Moreover, WSPNs can naturally be placed into the deep neural learning stack for time series, resulting in Whittle Networks, opening the likelihood toolbox for training deep neural models and inspecting their behaviour. Our experiments show that Whittle Networks can indeed capture complex dependencies between time series and provide a useful measure of uncertainty for neural networks. | https://proceedings.mlr.press/v139/yu21c.html | https://proceedings.mlr.press/v139/yu21c.html | https://proceedings.mlr.press/v139/yu21c.html | http://proceedings.mlr.press/v139/yu21c/yu21c.pdf | ICML 2021 | |
Deep Latent Graph Matching | Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li | Deep learning for graph matching (GM) has emerged as an important research topic due to its superior performance over traditional methods and insights it provides for solving other combinatorial problems on graph. While recent deep methods for GM extensively investigated effective node/edge feature learning or downstream GM solvers given such learned features, there is little existing work questioning if the fixed connectivity/topology typically constructed using heuristics (e.g., Delaunay or k-nearest) is indeed suitable for GM. From a learning perspective, we argue that the fixed topology may restrict the model capacity and thus potentially hinder the performance. To address this, we propose to learn the (distribution of) latent topology, which can better support the downstream GM task. We devise two latent graph generation procedures, one deterministic and one generative. Particularly, the generative procedure emphasizes the across-graph consistency and thus can be viewed as a matching-guided co-generative model. Our methods deliver superior performance over previous state-of-the-arts on public benchmarks, hence supporting our hypothesis. | https://proceedings.mlr.press/v139/yu21d.html | https://proceedings.mlr.press/v139/yu21d.html | https://proceedings.mlr.press/v139/yu21d.html | http://proceedings.mlr.press/v139/yu21d/yu21d.pdf | ICML 2021 | |
Learning Generalized Intersection Over Union for Dense Pixelwise Prediction | Jiaqian Yu, Jingtao Xu, Yiwei Chen, Weiming Li, Qiang Wang, Byungin Yoo, Jae-Joon Han | Intersection over union (IoU) score, also named Jaccard Index, is one of the most fundamental evaluation methods in machine learning. The original IoU computation cannot provide non-zero gradients and thus cannot be directly optimized by nowadays deep learning methods. Several recent works generalized IoU for bounding box regression, but they are not straightforward to adapt for pixelwise prediction. In particular, the original IoU fails to provide effective gradients for the non-overlapping and location-deviation cases, which results in performance plateau. In this paper, we propose PixIoU, a generalized IoU for pixelwise prediction that is sensitive to the distance for non-overlapping cases and the locations in prediction. We provide proofs that PixIoU holds many nice properties as the original IoU. To optimize the PixIoU, we also propose a loss function that is proved to be submodular, hence we can apply the Lovász functions, the efficient surrogates for submodular functions for learning this loss. Experimental results show consistent performance improvements by learning PixIoU over the original IoU for several different pixelwise prediction tasks on Pascal VOC, VOT-2020 and Cityscapes. | https://proceedings.mlr.press/v139/yu21e.html | https://proceedings.mlr.press/v139/yu21e.html | https://proceedings.mlr.press/v139/yu21e.html | http://proceedings.mlr.press/v139/yu21e/yu21e.pdf | ICML 2021 | |
Large Scale Private Learning via Low-rank Reparametrization | Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu | We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence. Specifically, we reparametrize each weight matrix with two \emph{gradient-carrier} matrices of small dimension and a \emph{residual weight} matrix. We argue that such reparametrization keeps the forward/backward process unchanged while enabling us to compute the projected gradient without computing the gradient itself. To learn with differential privacy, we design \emph{reparametrized gradient perturbation (RGP)} that perturbs the gradients on gradient-carrier matrices and reconstructs an update for the original weight from the noisy gradients. Importantly, we use historical updates to find the gradient-carrier matrices, whose optimality is rigorously justified under linear regression and empirically verified with deep learning tasks. RGP significantly reduces the memory cost and improves the utility. For example, we are the first able to apply differential privacy on the BERT model and achieve an average accuracy of $83.9%$ on four downstream tasks with $\epsilon=8$, which is within $5%$ loss compared to the non-private baseline but enjoys much lower privacy leakage risk. | https://proceedings.mlr.press/v139/yu21f.html | https://proceedings.mlr.press/v139/yu21f.html | https://proceedings.mlr.press/v139/yu21f.html | http://proceedings.mlr.press/v139/yu21f/yu21f.pdf | ICML 2021 | |
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity | Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang | Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on Federated Deep AUC Maximization (FDAM) is still limited. Compared with standard federated learning (FL) approaches that focus on decomposable minimization objectives, FDAM is more complicated due to its minimization objective is non-decomposable over individual examples. In this paper, we propose improved FDAM algorithms for heterogeneous data by solving the popular non-convex strongly-concave min-max formulation of DAM in a distributed fashion, which can also be applied to a class of non-convex strongly-concave min-max problems. A striking result of this paper is that the communication complexity of the proposed algorithm is a constant independent of the number of machines and also independent of the accuracy level, which improves an existing result by orders of magnitude. The experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets, and on medical chest X-ray images from different organizations. Our experiment shows that the performance of FDAM using data from multiple hospitals can improve the AUC score on testing data from a single hospital for detecting life-threatening diseases based on chest radiographs. | https://proceedings.mlr.press/v139/yuan21a.html | https://proceedings.mlr.press/v139/yuan21a.html | https://proceedings.mlr.press/v139/yuan21a.html | http://proceedings.mlr.press/v139/yuan21a/yuan21a.pdf | ICML 2021 | |
Neural Tangent Generalization Attacks | Chia-Hung Yuan, Shan-Hung Wu | The remarkable performance achieved by Deep Neural Networks (DNNs) in many applications is followed by the rising concern about data privacy and security. Since DNNs usually require large datasets to train, many practitioners scrape data from external sources such as the Internet. However, an external data owner may not be willing to let this happen, causing legal or ethical issues. In this paper, we study the generalization attacks against DNNs, where an attacker aims to slightly modify training data in order to spoil the training process such that a trained network lacks generalizability. These attacks can be performed by data owners and protect data from unexpected use. However, there is currently no efficient generalization attack against DNNs due to the complexity of a bilevel optimization involved. We propose the Neural Tangent Generalization Attack (NTGA) that, to the best of our knowledge, is the first work enabling clean-label, black-box generalization attack against DNNs. We conduct extensive experiments, and the empirical results demonstrate the effectiveness of NTGA. Our code and perturbed datasets are available at: https://github.com/lionelmessi6410/ntga. | https://proceedings.mlr.press/v139/yuan21b.html | https://proceedings.mlr.press/v139/yuan21b.html | https://proceedings.mlr.press/v139/yuan21b.html | http://proceedings.mlr.press/v139/yuan21b/yuan21b.pdf | ICML 2021 | |
On Explainability of Graph Neural Networks via Subgraph Explorations | Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji | We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explaining the importance of graph nodes or edges but ignore the substructures of graphs, which are more intuitive and human-intelligible. In this work, we propose a novel method, known as SubgraphX, to explain GNNs by identifying important subgraphs. Given a trained GNN model and an input graph, our SubgraphX explains its predictions by efficiently exploring different subgraphs with Monte Carlo tree search. To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs. To expedite computations, we propose efficient approximation schemes to compute Shapley values for graph data. Our work represents the first attempt to explain GNNs via identifying subgraphs explicitly and directly. Experimental results show that our SubgraphX achieves significantly improved explanations, while keeping computations at a reasonable level. | https://proceedings.mlr.press/v139/yuan21c.html | https://proceedings.mlr.press/v139/yuan21c.html | https://proceedings.mlr.press/v139/yuan21c.html | http://proceedings.mlr.press/v139/yuan21c/yuan21c.pdf | ICML 2021 | |
Federated Composite Optimization | Honglin Yuan, Manzil Zaheer, Sashank Reddi | Federated Learning (FL) is a distributed learning paradigm that scales on-device learning collaboratively and privately. Standard FL algorithms such as FEDAVG are primarily geared towards smooth unconstrained settings. In this paper, we study the Federated Composite Optimization (FCO) problem, in which the loss function contains a non-smooth regularizer. Such problems arise naturally in FL applications that involve sparsity, low-rank, monotonicity, or more general constraints. We first show that straightforward extensions of primal algorithms such as FedAvg are not well-suited for FCO since they suffer from the "curse of primal averaging," resulting in poor convergence. As a solution, we propose a new primal-dual algorithm, Federated Dual Averaging (FedDualAvg), which by employing a novel server dual averaging procedure circumvents the curse of primal averaging. Our theoretical analysis and empirical experiments demonstrate that FedDualAvg outperforms the other baselines. | https://proceedings.mlr.press/v139/yuan21d.html | https://proceedings.mlr.press/v139/yuan21d.html | https://proceedings.mlr.press/v139/yuan21d.html | http://proceedings.mlr.press/v139/yuan21d/yuan21d.pdf | ICML 2021 | |
Three Operator Splitting with a Nonconvex Loss Function | Alp Yurtsever, Varun Mangalick, Suvrit Sra | We consider the problem of minimizing the sum of three functions, one of which is nonconvex but differentiable, and the other two are convex but possibly nondifferentiable. We investigate the Three Operator Splitting method (TOS) of Davis & Yin (2017) with an aim to extend its theoretical guarantees for this nonconvex problem template. In particular, we prove convergence of TOS with nonasymptotic bounds on its nonstationarity and infeasibility errors. In contrast with the existing work on nonconvex TOS, our guarantees do not require additional smoothness assumptions on the terms comprising the objective; hence they cover instances of particular interest where the nondifferentiable terms are indicator functions. We also extend our results to a stochastic setting where we have access only to an unbiased estimator of the gradient. Finally, we illustrate the effectiveness of the proposed method through numerical experiments on quadratic assignment problems. | https://proceedings.mlr.press/v139/yurtsever21a.html | https://proceedings.mlr.press/v139/yurtsever21a.html | https://proceedings.mlr.press/v139/yurtsever21a.html | http://proceedings.mlr.press/v139/yurtsever21a/yurtsever21a.pdf | ICML 2021 | |
Grey-box Extraction of Natural Language Models | Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd, Boris Köpf | Model extraction attacks attempt to replicate a target machine learning model by querying its inference API. State-of-the-art attacks are learning-based and construct replicas by supervised training on the target model’s predictions, but an emerging class of attacks exploit algebraic properties to obtain high-fidelity replicas using orders of magnitude fewer queries. So far, these algebraic attacks have been limited to neural networks with few hidden layers and ReLU activations. In this paper we present algebraic and hybrid algebraic/learning-based attacks on large-scale natural language models. We consider a grey-box setting, targeting models with a pre-trained (public) encoder followed by a single (private) classification layer. Our key findings are that (i) with a frozen encoder, high-fidelity extraction is possible with a small number of in-distribution queries, making extraction attacks indistinguishable from legitimate use; (ii) when the encoder is fine-tuned, a hybrid learning-based/algebraic attack improves over the learning-based state-of-the-art without requiring additional queries. | https://proceedings.mlr.press/v139/zanella-beguelin21a.html | https://proceedings.mlr.press/v139/zanella-beguelin21a.html | https://proceedings.mlr.press/v139/zanella-beguelin21a.html | http://proceedings.mlr.press/v139/zanella-beguelin21a/zanella-beguelin21a.pdf | ICML 2021 | |
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL | Andrea Zanette | Several practical applications of reinforcement learning involve an agent learning from past data without the possibility of further exploration. Often these applications require us to 1) identify a near optimal policy or to 2) estimate the value of a target policy. For both tasks we derive exponential information-theoretic lower bounds in discounted infinite horizon MDPs with a linear function representation for the action value function even if 1) realizability holds, 2) the batch algorithm observes the exact reward and transition functions, and 3) the batch algorithm is given the best a priori data distribution for the problem class. Our work introduces a new ‘oracle + batch algorithm’ framework to prove lower bounds that hold for every distribution. The work shows an exponential separation between batch and online reinforcement learning. | https://proceedings.mlr.press/v139/zanette21a.html | https://proceedings.mlr.press/v139/zanette21a.html | https://proceedings.mlr.press/v139/zanette21a.html | http://proceedings.mlr.press/v139/zanette21a/zanette21a.pdf | ICML 2021 | |
Learning Binary Decision Trees by Argmin Differentiation | Valentina Zantedeschi, Matt Kusner, Vlad Niculae | We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node pruning) and continuous parameters (i.e., for tree split functions and prediction functions) simultaneously using argmin differentiation. We do so by sparsely relaxing a mixed-integer program for the discrete parameters, to allow gradients to pass through the program to continuous parameters. We derive customized algorithms to efficiently compute the forward and backward passes. This means that our tree learning procedure can be used as an (implicit) layer in arbitrary deep networks, and can be optimized with arbitrary loss functions. We demonstrate that our approach produces binary trees that are competitive with existing single tree and ensemble approaches, in both supervised and unsupervised settings. Further, apart from greedy approaches (which do not have competitive accuracies), our method is faster to train than all other tree-learning baselines we compare with. | https://proceedings.mlr.press/v139/zantedeschi21a.html | https://proceedings.mlr.press/v139/zantedeschi21a.html | https://proceedings.mlr.press/v139/zantedeschi21a.html | http://proceedings.mlr.press/v139/zantedeschi21a/zantedeschi21a.pdf | ICML 2021 | |
Barlow Twins: Self-Supervised Learning via Redundancy Reduction | Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stephane Deny | Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant solutions. Most current methods avoid such solutions by careful implementation details. We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors. The method is called Barlow Twins, owing to neuroscientist H. Barlow’s redundancy-reduction principle applied to a pair of identical networks. Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors. Barlow Twins outperforms previous methods on ImageNet for semi-supervised classification in the low-data regime, and is on par with current state of the art for ImageNet classification with a linear classifier head, and for transfer tasks of classification and object detection. | https://proceedings.mlr.press/v139/zbontar21a.html | https://proceedings.mlr.press/v139/zbontar21a.html | https://proceedings.mlr.press/v139/zbontar21a.html | http://proceedings.mlr.press/v139/zbontar21a/zbontar21a.pdf | ICML 2021 | |
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling | Zhanpeng Zeng, Yunyang Xiong, Sathya Ravi, Shailesh Acharya, Glenn M Fung, Vikas Singh | Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically on the sequence length. Training such models on longer sequences is expensive. In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at https://github.com/mlpen/YOSO. | https://proceedings.mlr.press/v139/zeng21a.html | https://proceedings.mlr.press/v139/zeng21a.html | https://proceedings.mlr.press/v139/zeng21a.html | http://proceedings.mlr.press/v139/zeng21a/zeng21a.pdf | ICML 2021 | |
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu | Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents. While significant achievements have been made in various perfect- and imperfect-information games, DouDizhu (a.k.a. Fighting the Landlord), a three-player card game, is still unsolved. DouDizhu is a very challenging domain with competition, collaboration, imperfect information, large state space, and particularly a massive set of possible actions where the legal actions vary significantly from turn to turn. Unfortunately, modern reinforcement learning algorithms mainly focus on simple and small action spaces, and not surprisingly, are shown not to make satisfactory progress in DouDizhu. In this work, we propose a conceptually simple yet effective DouDizhu AI system, namely DouZero, which enhances traditional Monte-Carlo methods with deep neural networks, action encoding, and parallel actors. Starting from scratch in a single server with four GPUs, DouZero outperformed all the existing DouDizhu AI programs in days of training and was ranked the first in the Botzone leaderboard among 344 AI agents. Through building DouZero, we show that classic Monte-Carlo methods can be made to deliver strong results in a hard domain with a complex action space. The code and an online demo are released at https://github.com/kwai/DouZero with the hope that this insight could motivate future work. | https://proceedings.mlr.press/v139/zha21a.html | https://proceedings.mlr.press/v139/zha21a.html | https://proceedings.mlr.press/v139/zha21a.html | http://proceedings.mlr.press/v139/zha21a/zha21a.pdf | ICML 2021 | |
DORO: Distributional and Outlier Robust Optimization | Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar | Many machine learning tasks involve subpopulation shift where the testing data distribution is a subpopulation of the training distribution. For such settings, a line of recent work has proposed the use of a variant of empirical risk minimization(ERM) known as distributionally robust optimization (DRO). In this work, we apply DRO to real, large-scale tasks with subpopulation shift, and observe that DRO performs relatively poorly, and moreover has severe instability. We identify one direct cause of this phenomenon: sensitivity of DRO to outliers in the datasets. To resolve this issue, we propose the framework of DORO, for Distributional and Outlier Robust Optimization. At the core of this approach is a refined risk function which prevents DRO from overfitting to potential outliers. We instantiate DORO for the Cressie-Read family of Rényi divergence, and delve into two specific instances of this family: CVaR and $\chi^2$-DRO. We theoretically prove the effectiveness of the proposed method, and empirically show that DORO improves the performance and stability of DRO with experiments on large modern datasets, thereby positively addressing the open question raised by Hashimoto et al., 2018. Codes are available at https://github.com/RuntianZ/doro. | https://proceedings.mlr.press/v139/zhai21a.html | https://proceedings.mlr.press/v139/zhai21a.html | https://proceedings.mlr.press/v139/zhai21a.html | http://proceedings.mlr.press/v139/zhai21a/zhai21a.pdf | ICML 2021 | |
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? | Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville | Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization? Peters et al. (2016) provides a positive answer for linear cases. In this paper, we use a functional modular probing method to analyze deep model structures under OOD setting. We demonstrate that even in biased models (which focus on spurious correlation) there still exist unbiased functional subnetworks. Furthermore, we articulate and confirm the functional lottery ticket hypothesis: the full network contains a subnetwork with proper structure that can achieve better OOD performance. We then propose Modular Risk Minimization to solve the subnetwork selection problem. Our algorithm learns the functional structure from a given dataset, and can be combined with any other OOD regularization methods. Experiments on various OOD generalization tasks corroborate the effectiveness of our method. | https://proceedings.mlr.press/v139/zhang21a.html | https://proceedings.mlr.press/v139/zhang21a.html | https://proceedings.mlr.press/v139/zhang21a.html | http://proceedings.mlr.press/v139/zhang21a/zhang21a.pdf | ICML 2021 | |
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons | Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang | It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small $\ell_\infty$-norm bounded adversarial perturbations. Although many attempts have been made, most previous works either can only provide empirical verification of the defense to a particular attack method, or can only develop a certified guarantee of the model robustness in limited scenarios. In this paper, we seek for a new approach to develop a theoretically principled neural network that inherently resists $\ell_\infty$ perturbations. In particular, we design a novel neuron that uses $\ell_\infty$-distance as its basic operation (which we call $\ell_\infty$-dist neuron), and show that any neural network constructed with $\ell_\infty$-dist neurons (called $\ell_{\infty}$-dist net) is naturally a 1-Lipschitz function with respect to $\ell_\infty$-norm. This directly provides a rigorous guarantee of the certified robustness based on the margin of prediction outputs. We then prove that such networks have enough expressive power to approximate any 1-Lipschitz function with robust generalization guarantee. We further provide a holistic training strategy that can greatly alleviate optimization difficulties. Experimental results show that using $\ell_{\infty}$-dist nets as basic building blocks, we consistently achieve state-of-the-art performance on commonly used datasets: 93.09% certified accuracy on MNIST ($\epsilon=0.3$), 35.42% on CIFAR-10 ($\epsilon=8/255$) and 16.31% on TinyImageNet ($\epsilon=1/255$). | https://proceedings.mlr.press/v139/zhang21b.html | https://proceedings.mlr.press/v139/zhang21b.html | https://proceedings.mlr.press/v139/zhang21b.html | http://proceedings.mlr.press/v139/zhang21b/zhang21b.pdf | ICML 2021 | |
Efficient Lottery Ticket Finding: Less Data is More | Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang | The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match the latter’s accuracies. However, finding winning tickets requires burdensome computations in the train-prune-retrain process, especially on large-scale datasets (e.g., ImageNet), restricting their practical benefits. This paper explores a new perspective on finding lottery tickets more efficiently, by doing so only with a specially selected subset of data, called Pruning-Aware Critical set (PrAC set), rather than using the full training set. The concept of PrAC set was inspired by the recent observation, that deep networks have samples that are either hard to memorize during training, or easy to forget during pruning. A PrAC set is thus hypothesized to capture those most challenging and informative examples for the dense model. We observe that a high-quality winning ticket can be found with training and pruning the dense network on the very compact PrAC set, which can substantially save training iterations for the ticket finding process. Extensive experiments validate our proposal across diverse datasets and network architectures. Specifically, on CIFAR-10, CIFAR-100, and Tiny ImageNet, we locate effective PrAC sets at 35.32% 78.19% of their training set sizes. On top of them, we can obtain the same competitive winning tickets for the corresponding dense networks, yet saving up to 82.85% 92.77%, 63.54% 74.92%, and 76.14% 86.56% training iterations, respectively. Crucially, we show that a PrAC set found is reusable across different network architectures, which can amortize the extra cost of finding PrAC sets, yielding a practical regime for efficient lottery ticket finding. | https://proceedings.mlr.press/v139/zhang21c.html | https://proceedings.mlr.press/v139/zhang21c.html | https://proceedings.mlr.press/v139/zhang21c.html | http://proceedings.mlr.press/v139/zhang21c/zhang21c.pdf | ICML 2021 | |
Robust Policy Gradient against Strong Data Corruption | Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun | We study the problem of robust reinforcement learning under adversarial corruption on both rewards and transitions. Our attack model assumes an \textit{adaptive} adversary who can arbitrarily corrupt the reward and transition at every step within an episode, for at most $\epsilon$-fraction of the learning episodes. Our attack model is strictly stronger than those considered in prior works. Our first result shows that no algorithm can find a better than $O(\epsilon)$-optimal policy under our attack model. Next, we show that surprisingly the natural policy gradient (NPG) method retains a natural robustness property if the reward corruption is bounded, and can find an $O(\sqrt{\epsilon})$-optimal policy. Consequently, we develop a Filtered Policy Gradient (FPG) algorithm that can tolerate even unbounded reward corruption and can find an $O(\epsilon^{1/4})$-optimal policy. We emphasize that FPG is the first that can achieve a meaningful learning guarantee when a constant fraction of episodes are corrupted. Complimentary to the theoretical results, we show that a neural implementation of FPG achieves strong robust learning performance on the MuJoCo continuous control benchmarks. | https://proceedings.mlr.press/v139/zhang21d.html | https://proceedings.mlr.press/v139/zhang21d.html | https://proceedings.mlr.press/v139/zhang21d.html | http://proceedings.mlr.press/v139/zhang21d/zhang21d.pdf | ICML 2021 | |
Near Optimal Reward-Free Reinforcement Learning | Zihan Zhang, Simon Du, Xiangyang Ji | We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple reward functions. This framework has two phases: in the exploration phase, the agent collects trajectories by interacting with the environment without using any reward signal; in the planning phase, the agent needs to return a near-optimal policy for arbitrary reward functions. %This framework is suitable for batch RL setting and the setting where there are multiple reward functions of interes We give a new efficient algorithm, \textbf{S}taged \textbf{S}ampling + \textbf{T}runcated \textbf{P}lanning (\algoname), which interacts with the environment at most $O\left( \frac{S^2A}{\epsilon^2}\poly\log\left(\frac{SAH}{\epsilon}\right) \right)$ episodes in the exploration phase, and guarantees to output a near-optimal policy for arbitrary reward functions in the planning phase, where $S$ is the size of state space, $A$ is the size of action space, $H$ is the planning horizon, and $\epsilon$ is the target accuracy relative to the total reward. Notably, our sample complexity scales only \emph{logarithmically} with $H$, in contrast to all existing results which scale \emph{polynomially} with $H$. Furthermore, this bound matches the minimax lower bound $\Omega\left(\frac{S^2A}{\epsilon^2}\right)$ up to logarithmic factors. Our results rely on three new techniques : 1) A new sufficient condition for the dataset to plan for an $\epsilon$-suboptimal policy % for any totally bounded reward function ; 2) A new way to plan efficiently under the proposed condition using soft-truncated planning; 3) Constructing extended MDP to maximize the truncated accumulative rewards efficiently. | https://proceedings.mlr.press/v139/zhang21e.html | https://proceedings.mlr.press/v139/zhang21e.html | https://proceedings.mlr.press/v139/zhang21e.html | http://proceedings.mlr.press/v139/zhang21e/zhang21e.pdf | ICML 2021 | |
Bayesian Attention Belief Networks | Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou | Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less explored due to the optimization difficulties or complicated model design. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights with a hierarchy of gamma distributions, and an encoder network by stacking Weibull distributions with a deterministic-upward-stochastic-downward structure to approximate the posterior. The resulting auto-encoding networks can be optimized in a differentiable way with a variational lower bound. It is simple to convert any models with deterministic attention, including pretrained ones, to the proposed Bayesian attention belief networks. On a variety of language understanding tasks, we show that our method outperforms deterministic attention and state-of-the-art stochastic attention in accuracy, uncertainty estimation, generalization across domains, and robustness to adversarial attacks. We further demonstrate the general applicability of our method on neural machine translation and visual question answering, showing great potential of incorporating our method into various attention-related tasks. | https://proceedings.mlr.press/v139/zhang21f.html | https://proceedings.mlr.press/v139/zhang21f.html | https://proceedings.mlr.press/v139/zhang21f.html | http://proceedings.mlr.press/v139/zhang21f/zhang21f.pdf | ICML 2021 | |
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models | Lily Zhang, Mark Goldstein, Rajesh Ranganath | Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD images than images from the training distribution. In this work, we explain why this behavior should be attributed to model misestimation. We first prove that no method can guarantee performance beyond random chance without assumptions on which out-distributions are relevant. We then interrogate the typical set hypothesis, the claim that relevant out-distributions can lie in high likelihood regions of the data distribution, and that OOD detection should be defined based on the data distribution’s typical set. We highlight the consequences implied by assuming support overlap between in- and out-distributions, as well as the arbitrariness of the typical set for OOD detection. Our results suggest that estimation error is a more plausible explanation than the misalignment between likelihood-based OOD detection and out-distributions of interest, and we illustrate how even minimal estimation error can lead to OOD detection failures, yielding implications for future work in deep generative modeling and OOD detection. | https://proceedings.mlr.press/v139/zhang21g.html | https://proceedings.mlr.press/v139/zhang21g.html | https://proceedings.mlr.press/v139/zhang21g.html | http://proceedings.mlr.press/v139/zhang21g/zhang21g.pdf | ICML 2021 | |
Poolingformer: Long Document Modeling with Pooling Attention | Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, Weizhu Chen | In this paper, we introduce a two-level attention schema, Poolingformer, for long document modeling. Its first level uses a smaller sliding window pattern to aggregate information from neighbors. Its second level employs a larger window to increase receptive fields with pooling attention to reduce both computational cost and memory consumption. We first evaluate Poolingformer on two long sequence QA tasks: the monolingual NQ and the multilingual TyDi QA. Experimental results show that Poolingformer sits atop three official leaderboards measured by F1, outperforming previous state-of-the-art models by 1.9 points (79.8 vs. 77.9) on NQ long answer, 1.9 points (79.5 vs. 77.6) on TyDi QA passage answer, and 1.6 points (67.6 vs. 66.0) on TyDi QA minimal answer. We further evaluate Poolingformer on a long sequence summarization task. Experimental results on the arXiv benchmark continue to demonstrate its superior performance. | https://proceedings.mlr.press/v139/zhang21h.html | https://proceedings.mlr.press/v139/zhang21h.html | https://proceedings.mlr.press/v139/zhang21h.html | http://proceedings.mlr.press/v139/zhang21h/zhang21h.pdf | ICML 2021 | |
Probabilistic Generating Circuits | Honghua Zhang, Brendan Juba, Guy Van Den Broeck | Generating functions, which are widely used in combinatorics and probability theory, encode function values into the coefficients of a polynomial. In this paper, we explore their use as a tractable probabilistic model, and propose probabilistic generating circuits (PGCs) for their efficient representation. PGCs are strictly more expressive efficient than many existing tractable probabilistic models, including determinantal point processes (DPPs), probabilistic circuits (PCs) such as sum-product networks, and tractable graphical models. We contend that PGCs are not just a theoretical framework that unifies vastly different existing models, but also show great potential in modeling realistic data. We exhibit a simple class of PGCs that are not trivially subsumed by simple combinations of PCs and DPPs, and obtain competitive performance on a suite of density estimation benchmarks. We also highlight PGCs’ connection to the theory of strongly Rayleigh distributions. | https://proceedings.mlr.press/v139/zhang21i.html | https://proceedings.mlr.press/v139/zhang21i.html | https://proceedings.mlr.press/v139/zhang21i.html | http://proceedings.mlr.press/v139/zhang21i/zhang21i.pdf | ICML 2021 | |
PAPRIKA: Private Online False Discovery Rate Control | Wanrong Zhang, Gautam Kamath, Rachel Cummings | In hypothesis testing, a \emph{false discovery} occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability of a false discovery increases as more tests are performed. Thus the problem of \emph{False Discovery Rate (FDR) control} is to find a procedure for testing multiple hypotheses that accounts for this effect in determining the set of hypotheses to reject. The goal is to minimize the number (or fraction) of false discoveries, while maintaining a high true positive rate (i.e., correct discoveries). In this work, we study False Discovery Rate (FDR) control in multiple hypothesis testing under the constraint of differential privacy for the sample. Unlike previous work in this direction, we focus on the \emph{online setting}, meaning that a decision about each hypothesis must be made immediately after the test is performed, rather than waiting for the output of all tests as in the offline setting. We provide new private algorithms based on state-of-the-art results in non-private online FDR control. Our algorithms have strong provable guarantees for privacy and statistical performance as measured by FDR and power. We also provide experimental results to demonstrate the efficacy of our algorithms in a variety of data environments. | https://proceedings.mlr.press/v139/zhang21j.html | https://proceedings.mlr.press/v139/zhang21j.html | https://proceedings.mlr.press/v139/zhang21j.html | http://proceedings.mlr.press/v139/zhang21j/zhang21j.pdf | ICML 2021 | |
Learning from Noisy Labels with No Change to the Training Process | Mingyuan Zhang, Jane Lee, Shivani Agarwal | There has been much interest in recent years in developing learning algorithms that can learn accurate classifiers from data with noisy labels. A widely-studied noise model is that of \emph{class-conditional noise} (CCN), wherein a label $y$ is flipped to a label $\tilde{y}$ with some associated noise probability that depends on both $y$ and $\tilde{y}$. In the multiclass setting, all previously proposed algorithms under the CCN model involve changing the training process, by introducing a ‘noise-correction’ to the surrogate loss to be minimized over the noisy training examples. In this paper, we show that this is really unnecessary: one can simply perform class probability estimation (CPE) on the noisy examples, e.g. using a standard (multiclass) logistic regression algorithm, and then apply noise-correction only in the final prediction step. This means that the training algorithm itself does not need any change, and one can simply use standard off-the-shelf implementations with no modification to the code for training. Our approach can handle general multiclass loss matrices, including the usual 0-1 loss but also other losses such as those used for ordinal regression problems. We also provide a quantitative regret transfer bound, which bounds the target regret on the true distribution in terms of the CPE regret on the noisy distribution; in doing so, we extend the notion of strong properness introduced for binary losses by Agarwal (2014) to the multiclass case. Our bound suggests that the sample complexity of learning under CCN increases as the noise matrix approaches singularity. We also provide fixes and potential improvements for noise estimation methods that involve computing anchor points. Our experiments confirm our theoretical findings. | https://proceedings.mlr.press/v139/zhang21k.html | https://proceedings.mlr.press/v139/zhang21k.html | https://proceedings.mlr.press/v139/zhang21k.html | http://proceedings.mlr.press/v139/zhang21k/zhang21k.pdf | ICML 2021 | |
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation | Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li | Boundary based blackbox attack has been recognized as practical and effective, given that an attacker only needs to access the final model prediction. However, the query efficiency of it is in general high especially for high dimensional image data. In this paper, we show that such efficiency highly depends on the scale at which the attack is applied, and attacking at the optimal scale significantly improves the efficiency. In particular, we propose a theoretical framework to analyze and show three key characteristics to improve the query efficiency. We prove that there exists an optimal scale for projective gradient estimation. Our framework also explains the satisfactory performance achieved by existing boundary black-box attacks. Based on our theoretical framework, we propose Progressive-Scale enabled projective Boundary Attack (PSBA) to improve the query efficiency via progressive scaling techniques. In particular, we employ Progressive-GAN to optimize the scale of projections, which we call PSBA-PGAN. We evaluate our approach on both spatial and frequency scales. Extensive experiments on MNIST, CIFAR-10, CelebA, and ImageNet against different models including a real-world face recognition API show that PSBA-PGAN significantly outperforms existing baseline attacks in terms of query efficiency and attack success rate. We also observe relatively stable optimal scales for different models and datasets. The code is publicly available at https://github.com/AI-secure/PSBA. | https://proceedings.mlr.press/v139/zhang21l.html | https://proceedings.mlr.press/v139/zhang21l.html | https://proceedings.mlr.press/v139/zhang21l.html | http://proceedings.mlr.press/v139/zhang21l/zhang21l.pdf | ICML 2021 | |
FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning | Tianhao Zhang, Yueheng Li, Chen Wang, Guangming Xie, Zongqing Lu | Value decomposition recently injects vigorous vitality into multi-agent actor-critic methods. However, existing decomposed actor-critic methods cannot guarantee the convergence of global optimum. In this paper, we present a novel multi-agent actor-critic method, FOP, which can factorize the optimal joint policy induced by maximum-entropy multi-agent reinforcement learning (MARL) into individual policies. Theoretically, we prove that factorized individual policies of FOP converge to the global optimum. Empirically, in the well-known matrix game and differential game, we verify that FOP can converge to the global optimum for both discrete and continuous action spaces. We also evaluate FOP on a set of StarCraft II micromanagement tasks, and demonstrate that FOP substantially outperforms state-of-the-art decomposed value-based and actor-critic methods. | https://proceedings.mlr.press/v139/zhang21m.html | https://proceedings.mlr.press/v139/zhang21m.html | https://proceedings.mlr.press/v139/zhang21m.html | http://proceedings.mlr.press/v139/zhang21m/zhang21m.pdf | ICML 2021 | |
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization | Yivan Zhang, Gang Niu, Masashi Sugiyama | Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks. In this work, we propose a theoretically grounded method that can estimate the noise transition matrix and learn a classifier simultaneously, without relying on the error-prone noisy class-posterior estimation. Concretely, inspired by the characteristics of the stochastic label corruption process, we propose total variation regularization, which encourages the predicted probabilities to be more distinguishable from each other. Under mild assumptions, the proposed method yields a consistent estimator of the transition matrix. We show the effectiveness of the proposed method through experiments on benchmark and real-world datasets. | https://proceedings.mlr.press/v139/zhang21n.html | https://proceedings.mlr.press/v139/zhang21n.html | https://proceedings.mlr.press/v139/zhang21n.html | http://proceedings.mlr.press/v139/zhang21n/zhang21n.pdf | ICML 2021 | |
Quantile Bandits for Best Arms Identification | Mengyan Zhang, Cheng Soon Ong | We consider a variant of the best arm identification task in stochastic multi-armed bandits. Motivated by risk-averse decision-making problems, our goal is to identify a set of $m$ arms with the highest $\tau$-quantile values within a fixed budget. We prove asymmetric two-sided concentration inequalities for order statistics and quantiles of random variables that have non-decreasing hazard rate, which may be of independent interest. With these inequalities, we analyse a quantile version of Successive Accepts and Rejects (Q-SAR). We derive an upper bound for the probability of arm misidentification, the first justification of a quantile based algorithm for fixed budget multiple best arms identification. We show illustrative experiments for best arm identification. | https://proceedings.mlr.press/v139/zhang21o.html | https://proceedings.mlr.press/v139/zhang21o.html | https://proceedings.mlr.press/v139/zhang21o.html | http://proceedings.mlr.press/v139/zhang21o/zhang21o.pdf | ICML 2021 | |
Towards Better Robust Generalization with Shift Consistency Regularization | Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi | While adversarial training becomes one of the most promising defending approaches against adversarial attacks for deep neural networks, the conventional wisdom through robust optimization may usually not guarantee good generalization for robustness. Concerning with robust generalization over unseen adversarial data, this paper investigates adversarial training from a novel perspective of shift consistency in latent space. We argue that the poor robust generalization of adversarial training is owing to the significantly dispersed latent representations generated by training and test adversarial data, as the adversarial perturbations push the latent features of natural examples in the same class towards diverse directions. This is underpinned by the theoretical analysis of the robust generalization gap, which is upper-bounded by the standard one over the natural data and a term of feature inconsistent shift caused by adversarial perturbation {–} a measure of latent dispersion. Towards better robust generalization, we propose a new regularization method {–} shift consistency regularization (SCR) {–} to steer the same-class latent features of both natural and adversarial data into a common direction during adversarial training. The effectiveness of SCR in adversarial training is evaluated through extensive experiments over different datasets, such as CIFAR-10, CIFAR-100, and SVHN, against several competitive methods. | https://proceedings.mlr.press/v139/zhang21p.html | https://proceedings.mlr.press/v139/zhang21p.html | https://proceedings.mlr.press/v139/zhang21p.html | http://proceedings.mlr.press/v139/zhang21p/zhang21p.pdf | ICML 2021 | |
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion | Yiming Zhang, Keith W Ross | We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policies. We show that previous work based on the discounted return (Schulman et al. 2015, Achiam et al. 2017) results in a non-meaningful lower bound in the average reward setting. By addressing the average-reward criterion directly, we then derive a novel bound which depends on the average divergence between the policies and on Kemeny’s constant. Based on this bound, we develop an iterative procedure which produces a sequence of monotonically improved policies for the average reward criterion. This iterative procedure can then be combined with classic Deep Reinforcement Learning (DRL) methods, resulting in practical DRL algorithms that target the long-run average reward criterion. In particular, we demonstrate that Average-Reward TRPO (ATRPO), which adapts the on-policy TRPO algorithm to the average-reward criterion, significantly outperforms TRPO in the most challenging MuJuCo environments. | https://proceedings.mlr.press/v139/zhang21q.html | https://proceedings.mlr.press/v139/zhang21q.html | https://proceedings.mlr.press/v139/zhang21q.html | http://proceedings.mlr.press/v139/zhang21q/zhang21q.pdf | ICML 2021 | |
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution | Zhaoyang Zhang, Wenqi Shao, Jinwei Gu, Xiaogang Wang, Ping Luo | Model quantization is challenging due to many tedious hyper-parameters such as precision (bitwidth), dynamic range (minimum and maximum discrete values) and stepsize (interval between discrete values). Unlike prior arts that carefully tune these values, we present a fully differentiable approach to learn all of them, named Differentiable Dynamic Quantization (DDQ), which has several benefits. (1) DDQ is able to quantize challenging lightweight architectures like MobileNets, where different layers prefer different quantization parameters. (2) DDQ is hardware-friendly and can be easily implemented using low-precision matrix-vector multiplication, making it capable in many hardware such as ARM. (3) Extensive experiments show that DDQ outperforms prior arts on many networks and benchmarks, especially when models are already efficient and compact. e.g., DDQ is the first approach that achieves lossless 4-bit quantization for MobileNetV2 on ImageNet. | https://proceedings.mlr.press/v139/zhang21r.html | https://proceedings.mlr.press/v139/zhang21r.html | https://proceedings.mlr.press/v139/zhang21r.html | http://proceedings.mlr.press/v139/zhang21r/zhang21r.pdf | ICML 2021 | |
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients | Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Ehsan M Abbasnejad, Reza Haffari | Differentiable ARchiTecture Search(DARTS) has recently become the mainstream in the neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-based bi-level optimization, DARTS alternately optimizes the inner model weights and the outer architecture parameter in a weight-sharing supernet. A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation. While much has been discussed about several potentially fatal factors in DARTS, the architecture gradient, a.k.a. hypergradient, has received less attention. In this paper, we tackle the hypergradient computation in DARTS based on the implicit function theorem, making it only depends on the obtained solution to the inner-loop optimization and agnostic to the optimization path. To further reduce the computational requirements, we formulate a stochastic hypergradient approximation for differentiable NAS, and theoretically show that the architecture optimization with the proposed method is expected to converge to a stationary point. Comprehensive experiments on two NAS benchmark search spaces and the common NAS search space verify the effectiveness of our proposed method. It leads to architectures outperforming, with large margins, those learned by the baseline methods. | https://proceedings.mlr.press/v139/zhang21s.html | https://proceedings.mlr.press/v139/zhang21s.html | https://proceedings.mlr.press/v139/zhang21s.html | http://proceedings.mlr.press/v139/zhang21s/zhang21s.pdf | ICML 2021 | |
Deep Coherent Exploration for Continuous Control | Yijie Zhang, Herke Van Hoof | In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory. In prior work, it has been shown that with linear policies, a more balanced trade-off between these two exploration strategies is beneficial. However, that method did not scale to policies using deep neural networks. In this paper, we introduce deep coherent exploration, a general and scalable exploration framework for deep RL algorithms for continuous control, that generalizes step-based and trajectory-based exploration. This framework models the last layer parameters of the policy network as latent variables and uses a recursive inference step within the policy update to handle these latent variables in a scalable manner. We find that deep coherent exploration improves the speed and stability of learning of A2C, PPO, and SAC on several continuous control tasks. | https://proceedings.mlr.press/v139/zhang21t.html | https://proceedings.mlr.press/v139/zhang21t.html | https://proceedings.mlr.press/v139/zhang21t.html | http://proceedings.mlr.press/v139/zhang21t/zhang21t.pdf | ICML 2021 | |
Average-Reward Off-Policy Policy Evaluation with Function Approximation | Shangtong Zhang, Yi Wan, Richard S Sutton, Shimon Whiteson | We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value function. For this problem, bootstrapping is necessary and, along with off-policy learning and FA, results in the deadly triad (Sutton & Barto, 2018). To address the deadly triad, we propose two novel algorithms, reproducing the celebrated success of Gradient TD algorithms in the average-reward setting. In terms of estimating the differential value function, the algorithms are the first convergent off-policy linear function approximation algorithms. In terms of estimating the reward rate, the algorithms are the first convergent off-policy linear function approximation algorithms that do not require estimating the density ratio. We demonstrate empirically the advantage of the proposed algorithms, as well as their nonlinear variants, over a competitive density-ratio-based approach, in a simple domain as well as challenging robot simulation tasks. | https://proceedings.mlr.press/v139/zhang21u.html | https://proceedings.mlr.press/v139/zhang21u.html | https://proceedings.mlr.press/v139/zhang21u.html | http://proceedings.mlr.press/v139/zhang21u/zhang21u.pdf | ICML 2021 | |
Matrix Sketching for Secure Collaborative Machine Learning | Mengjiao Zhang, Shusen Wang | Collaborative learning allows participants to jointly train a model without data sharing. To update the model parameters, the central server broadcasts model parameters to the clients, and the clients send updating directions such as gradients to the server. While data do not leave a client device, the communicated gradients and parameters will leak a client’s privacy. Attacks that infer clients’ privacy from gradients and parameters have been developed by prior work. Simple defenses such as dropout and differential privacy either fail to defend the attacks or seriously hurt test accuracy. We propose a practical defense which we call Double-Blind Collaborative Learning (DBCL). The high-level idea is to apply random matrix sketching to the parameters (aka weights) and re-generate random sketching after each iteration. DBCL prevents clients from conducting gradient-based privacy inferences which are the most effective attacks. DBCL works because from the attacker’s perspective, sketching is effectively random noise that outweighs the signal. Notably, DBCL does not much increase computation and communication costs and does not hurt test accuracy at all. | https://proceedings.mlr.press/v139/zhang21v.html | https://proceedings.mlr.press/v139/zhang21v.html | https://proceedings.mlr.press/v139/zhang21v.html | http://proceedings.mlr.press/v139/zhang21v/zhang21v.pdf | ICML 2021 | |
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration | Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang | Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks. Despite recent progress, efficient exploration in meta-RL remains a key challenge in sparse-reward tasks, as it requires quickly finding informative task-relevant experiences in both meta-training and adaptation. To address this challenge, we explicitly model an exploration policy learning problem for meta-RL, which is separated from exploitation policy learning, and introduce a novel empowerment-driven exploration objective, which aims to maximize information gain for task identification. We derive a corresponding intrinsic reward and develop a new off-policy meta-RL framework, which efficiently learns separate context-aware exploration and exploitation policies by sharing the knowledge of task inference. Experimental evaluation shows that our meta-RL method significantly outperforms state-of-the-art baselines on various sparse-reward MuJoCo locomotion tasks and more complex sparse-reward Meta-World tasks. | https://proceedings.mlr.press/v139/zhang21w.html | https://proceedings.mlr.press/v139/zhang21w.html | https://proceedings.mlr.press/v139/zhang21w.html | http://proceedings.mlr.press/v139/zhang21w/zhang21w.pdf | ICML 2021 | |
World Model as a Graph: Learning Latent Landmarks for Planning | Lunjun Zhang, Ge Yang, Bradly C Stadie | Planning, the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems, is a hallmark of human intelligence. While deep reinforcement learning (RL) has shown great promise for solving relatively straightforward control tasks, it remains an open problem how to best incorporate planning into existing deep RL paradigms to handle increasingly complex environments. One prominent framework, Model-Based RL, learns a world model and plans using step-by-step virtual rollouts. This type of world model quickly diverges from reality when the planning horizon increases, thus struggling at long-horizon planning. How can we learn world models that endow agents with the ability to do temporally extended reasoning? In this work, we propose to learn graph-structured world models composed of sparse, multi-step transitions. We devise a novel algorithm to learn latent landmarks that are scattered (in terms of reachability) across the goal space as the nodes on the graph. In this same graph, the edges are the reachability estimates distilled from Q-functions. On a variety of high-dimensional continuous control tasks ranging from robotic manipulation to navigation, we demonstrate that our method, named L3P, significantly outperforms prior work, and is oftentimes the only method capable of leveraging both the robustness of model-free RL and generalization of graph-search algorithms. We believe our work is an important step towards scalable planning in reinforcement learning. | https://proceedings.mlr.press/v139/zhang21x.html | https://proceedings.mlr.press/v139/zhang21x.html | https://proceedings.mlr.press/v139/zhang21x.html | http://proceedings.mlr.press/v139/zhang21x/zhang21x.pdf | ICML 2021 | |
Breaking the Deadly Triad with a Target Network | Shangtong Zhang, Hengshuai Yao, Shimon Whiteson | The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for the conventional wisdom that a target network stabilizes training. We first propose and analyze a novel target network update rule which augments the commonly used Polyak-averaging style update with two projections. We then apply the target network and ridge regularization in several divergent algorithms and show their convergence to regularized TD fixed points. Those algorithms are off-policy with linear function approximation and bootstrapping, spanning both policy evaluation and control, as well as both discounted and average-reward settings. In particular, we provide the first convergent linear $Q$-learning algorithms under nonrestrictive and changing behavior policies without bi-level optimization. | https://proceedings.mlr.press/v139/zhang21y.html | https://proceedings.mlr.press/v139/zhang21y.html | https://proceedings.mlr.press/v139/zhang21y.html | http://proceedings.mlr.press/v139/zhang21y/zhang21y.pdf | ICML 2021 | |
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference | Shumao Zhang, Pengchuan Zhang, Thomas Y Hou | We propose a Multiscale Invertible Generative Network (MsIGN) and associated training algorithm that leverages multiscale structure to solve high-dimensional Bayesian inference. To address the curse of dimensionality, MsIGN exploits the low-dimensional nature of the posterior, and generates samples from coarse to fine scale (low to high dimension) by iteratively upsampling and refining samples. MsIGN is trained in a multi-stage manner to minimize the Jeffreys divergence, which avoids mode dropping in high-dimensional cases. On two high-dimensional Bayesian inverse problems, we show superior performance of MsIGN over previous approaches in posterior approximation and multiple mode capture. On the natural image synthesis task, MsIGN achieves superior performance in bits-per-dimension over baseline models and yields great interpret-ability of its neurons in intermediate layers. | https://proceedings.mlr.press/v139/zhang21z.html | https://proceedings.mlr.press/v139/zhang21z.html | https://proceedings.mlr.press/v139/zhang21z.html | http://proceedings.mlr.press/v139/zhang21z/zhang21z.pdf | ICML 2021 | |
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation | Qian Zhang, Yilin Zheng, Jean Honorio | In this paper, we study meta learning for support (i.e., the set of non-zero entries) recovery in high-dimensional precision matrix estimation where we reduce the sufficient sample complexity in a novel task with the information learned from other auxiliary tasks. In our setup, each task has a different random true precision matrix, each with a possibly different support. We assume that the union of the supports of all the true precision matrices (i.e., the true support union) is small in size. We propose to pool all the samples from different tasks, and \emph{improperly} estimate a single precision matrix by minimizing the $\ell_1$-regularized log-determinant Bregman divergence. We show that with high probability, the support of the \emph{improperly} estimated single precision matrix is equal to the true support union, provided a sufficient number of samples per task $n \in O((\log N)/K)$, for $N$-dimensional vectors and $K$ tasks. That is, one requires less samples per task when more tasks are available. We prove a matching information-theoretic lower bound for the necessary number of samples, which is $n \in \Omega((\log N)/K)$, and thus, our algorithm is minimax optimal. Then for the novel task, we prove that the minimization of the $\ell_1$-regularized log-determinant Bregman divergence with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to $O(\log(|S_{\text{off}}|))$ where $|S_{\text{off}}|$ is the number of off-diagonal elements in the support union and is much less than $N$ for sparse matrices. We also prove a matching information-theoretic lower bound of $\Omega(\log(|S_{\text{off}}|))$ for the necessary number of samples. | https://proceedings.mlr.press/v139/zhang21aa.html | https://proceedings.mlr.press/v139/zhang21aa.html | https://proceedings.mlr.press/v139/zhang21aa.html | http://proceedings.mlr.press/v139/zhang21aa/zhang21aa.pdf | ICML 2021 | |
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity | Zihan Zhang, Yuan Zhou, Xiangyang Ji | In this paper we consider the problem of learning an $\epsilon$-optimal policy for a discounted Markov Decision Process (MDP). Given an MDP with $S$ states, $A$ actions, the discount factor $\gamma \in (0,1)$, and an approximation threshold $\epsilon > 0$, we provide a model-free algorithm to learn an $\epsilon$-optimal policy with sample complexity $\tilde{O}(\frac{SA\ln(1/p)}{\epsilon^2(1-\gamma)^{5.5}})$ \footnote{In this work, the notation $\tilde{O}(\cdot)$ hides poly-logarithmic factors of $S,A,1/(1-\gamma)$, and $1/\epsilon$.} and success probability $(1-p)$. For small enough $\epsilon$, we show an improved algorithm with sample complexity $\tilde{O}(\frac{SA\ln(1/p)}{\epsilon^2(1-\gamma)^{3}})$. While the first bound improves upon all known model-free algorithms and model-based ones with tight dependence on $S$, our second algorithm beats all known sample complexity bounds and matches the information theoretic lower bound up to logarithmic factors. | https://proceedings.mlr.press/v139/zhang21ab.html | https://proceedings.mlr.press/v139/zhang21ab.html | https://proceedings.mlr.press/v139/zhang21ab.html | http://proceedings.mlr.press/v139/zhang21ab/zhang21ab.pdf | ICML 2021 | |
Learning to Rehearse in Long Sequence Memorization | Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao | Existing reasoning tasks often have an important assumption that the input contents can be always accessed while reasoning, requiring unlimited storage resources and suffering from severe time delay on long sequences. To achieve efficient reasoning on long sequences with limited storage resources, memory augmented neural networks introduce a human-like write-read memory to compress and memorize the long input sequence in one pass, trying to answer subsequent queries only based on the memory. But they have two serious drawbacks: 1) they continually update the memory from current information and inevitably forget the early contents; 2) they do not distinguish what information is important and treat all contents equally. In this paper, we propose the Rehearsal Memory (RM) to enhance long-sequence memorization by self-supervised rehearsal with a history sampler. To alleviate the gradual forgetting of early information, we design self-supervised rehearsal training with recollection and familiarity tasks. Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information. We evaluate the performance of our rehearsal memory by the synthetic bAbI task and several downstream tasks, including text/video question answering and recommendation on long sequences. | https://proceedings.mlr.press/v139/zhang21ac.html | https://proceedings.mlr.press/v139/zhang21ac.html | https://proceedings.mlr.press/v139/zhang21ac.html | http://proceedings.mlr.press/v139/zhang21ac/zhang21ac.pdf | ICML 2021 | |
Dataset Condensation with Differentiable Siamese Augmentation | Bo Zhao, Hakan Bilen | In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this paper, we focus on condensing large training sets into significantly smaller synthetic sets which can be used to train deep neural networks from scratch with minimum drop in performance. Inspired from the recent training set synthesis methods, we propose Differentiable Siamese Augmentation that enables effective use of data augmentation to synthesize more informative synthetic images and thus achieves better performance when training networks with augmentations. Experiments on multiple image classification benchmarks demonstrate that the proposed method obtains substantial gains over the state-of-the-art, 7% improvements on CIFAR10 and CIFAR100 datasets. We show with only less than 1% data that our method achieves 99.6%, 94.9%, 88.5%, 71.5% relative performance on MNIST, FashionMNIST, SVHN, CIFAR10 respectively. We also explore the use of our method in continual learning and neural architecture search, and show promising results. | https://proceedings.mlr.press/v139/zhao21a.html | https://proceedings.mlr.press/v139/zhao21a.html | https://proceedings.mlr.press/v139/zhao21a.html | http://proceedings.mlr.press/v139/zhao21a/zhao21a.pdf | ICML 2021 | |
Joining datasets via data augmentation in the label space for neural networks | Junbo Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu, Gang Chen | Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference. In this article, we are interested in systematic ways to join datasets that are made of similar purposes. Unlike previous published works that ubiquitously conduct the dataset joining in the uninterpretable latent vectorial space, the core to our method is an augmentation procedure in the label space. The primary challenge to address the label space for dataset joining is the discrepancy between labels: non-overlapping label annotation sets, different labeling granularity or hierarchy and etc. Notably we propose a new technique leveraging artificially created knowledge graph, recurrent neural networks and policy gradient that successfully achieve the dataset joining in the label space. Empirical results on both image and text classification justify the validity of our approach. | https://proceedings.mlr.press/v139/zhao21b.html | https://proceedings.mlr.press/v139/zhao21b.html | https://proceedings.mlr.press/v139/zhao21b.html | http://proceedings.mlr.press/v139/zhao21b/zhao21b.pdf | ICML 2021 | |
Calibrate Before Use: Improving Few-shot Performance of Language Models | Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh | GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are placed near the end of the prompt or are common in the pre-training data. To mitigate this, we first estimate the model’s bias towards each answer by asking for its prediction when given a training prompt and a content-free test input such as "N/A". We then fit calibration parameters that cause the prediction for this input to be uniform across answers. On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2’s accuracy (up to 30.0% absolute) across different choices of the prompt, while also making learning considerably more stable. | https://proceedings.mlr.press/v139/zhao21c.html | https://proceedings.mlr.press/v139/zhao21c.html | https://proceedings.mlr.press/v139/zhao21c.html | http://proceedings.mlr.press/v139/zhao21c/zhao21c.pdf | ICML 2021 | |
Few-Shot Neural Architecture Search | Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo | Efficient evaluation of a network architecture drawn from a large search space remains a key challenge in Neural Architecture Search (NAS). Vanilla NAS evaluates each architecture by training from scratch, which gives the true performance but is extremely time-consuming. Recently, one-shot NAS substantially reduces the computation cost by training only one supernetwork, a.k.a. supernet, to approximate the performance of every architecture in the search space via weight-sharing. However, the performance estimation can be very inaccurate due to the co-adaption among operations. In this paper, we propose few-shot NAS that uses multiple supernetworks, called sub-supernet, each covering different regions of the search space to alleviate the undesired co-adaption. Compared to one-shot NAS, few-shot NAS improves the accuracy of architecture evaluation with a small increase of evaluation cost. With only up to 7 sub-supernets, few-shot NAS establishes new SoTAs: on ImageNet, it finds models that reach 80.5% top-1 accuracy at 600 MB FLOPS and 77.5% top-1 accuracy at 238 MFLOPS; on CIFAR10, it reaches 98.72% top-1 accuracy without using extra data or transfer learning. In Auto-GAN, few-shot NAS outperforms the previously published results by up to 20%. Extensive experiments show that few-shot NAS significantly improves various one-shot methods, including 4 gradient-based and 6 search-based methods on 3 different tasks in NasBench-201 and NasBench1-shot-1. | https://proceedings.mlr.press/v139/zhao21d.html | https://proceedings.mlr.press/v139/zhao21d.html | https://proceedings.mlr.press/v139/zhao21d.html | http://proceedings.mlr.press/v139/zhao21d/zhao21d.pdf | ICML 2021 | |
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks | Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, Da Yan | Recent findings have shown multiple graph learning models, such as graph classification and graph matching, are highly vulnerable to adversarial attacks, i.e. small input perturbations in graph structures and node attributes can cause the model failures. Existing defense techniques often defend specific attacks on particular multiple graph learning tasks. This paper proposes an attack-agnostic graph-adaptive 1-Lipschitz neural network, ERNN, for improving the robustness of deep multiple graph learning while achieving remarkable expressive power. A K_l-Lipschitz Weibull activation function is designed to enforce the gradient norm as K_l at layer l. The nearest matrix orthogonalization and polar decomposition techniques are utilized to constraint the weight norm as 1/K_l and make the norm-constrained weight close to the original weight. The theoretical analysis is conducted to derive lower and upper bounds of feasible K_l under the 1-Lipschitz constraint. The combination of norm-constrained weight and activation function leads to the 1-Lipschitz neural network for expressive and robust multiple graph learning. | https://proceedings.mlr.press/v139/zhao21e.html | https://proceedings.mlr.press/v139/zhao21e.html | https://proceedings.mlr.press/v139/zhao21e.html | http://proceedings.mlr.press/v139/zhao21e/zhao21e.pdf | ICML 2021 | |
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation | Renjie Zheng, Junkun Chen, Mingbo Ma, Liang Huang | Recently, representation learning for text and speech has successfully improved many language related tasks. However, all existing methods suffer from two limitations: (a) they only learn from one input modality, while a unified representation for both speech and text is needed by tasks such as end-to-end speech translation, and as a result, (b) they can not exploit various large-scale text and speech data and their performance is limited by the scarcity of parallel speech translation data. To address these problems, we propose a Fused Acoustic and Text Masked Language Model (FAT-MLM) which jointly learns a unified representation for both acoustic and text input from various types of corpora including parallel data for speech recognition and machine translation, and even pure speech and text data. Within this cross-modal representation learning framework, we further present an end-to-end model for Fused Acoustic and Text Speech Translation (FAT-ST). Experiments on three translation directions show that by fine-tuning from FAT-MLM, our proposed speech translation models substantially improve translation quality by up to +5.9 BLEU. | https://proceedings.mlr.press/v139/zheng21a.html | https://proceedings.mlr.press/v139/zheng21a.html | https://proceedings.mlr.press/v139/zheng21a.html | http://proceedings.mlr.press/v139/zheng21a/zheng21a.pdf | ICML 2021 | |
Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination | Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang | Even with a still image, humans can ratiocinate various visual cause-and-effect descriptions before, at present, and after, as well as beyond the given image. However, it is challenging for models to achieve such task–the visual event ratiocination, owing to the limitations of time and space. To this end, we propose a novel multi-modal model, Hypergraph-Enhanced Graph Reasoning. First it represents the contents from the same modality as a semantic graph and mines the intra-modality relationship, therefore breaking the limitations in the spatial domain. Then, we introduce the Graph Self-Attention Enhancement. On the one hand, this enables semantic graph representations from different modalities to enhance each other and captures the inter-modality relationship along the line. On the other hand, it utilizes our built multi-modal hypergraphs in different moments to boost individual semantic graph representations, and breaks the limitations in the temporal domain. Our method illustrates the case of "two heads are better than one" in the sense that semantic graph representations with the help of the proposed enhancement mechanism are more robust than those without. Finally, we re-project these representations and leverage their outcomes to generate textual cause-and-effect descriptions. Experimental results show that our model achieves significantly higher performance in comparison with other state-of-the-arts. | https://proceedings.mlr.press/v139/zheng21b.html | https://proceedings.mlr.press/v139/zheng21b.html | https://proceedings.mlr.press/v139/zheng21b.html | http://proceedings.mlr.press/v139/zheng21b/zheng21b.pdf | ICML 2021 | |
How Framelets Enhance Graph Neural Networks | Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montufar | This paper presents a new approach for assembling graph neural networks based on framelet transforms. The latter provides a multi-scale representation for graph-structured data. We decompose an input graph into low-pass and high-pass frequencies coefficients for network training, which then defines a framelet-based graph convolution. The framelet decomposition naturally induces a graph pooling strategy by aggregating the graph feature into low-pass and high-pass spectra, which considers both the feature values and geometry of the graph data and conserves the total information. The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks. Moreover, we propose shrinkage as a new activation for the framelet convolution, which thresholds high-frequency information at different scales. Compared to ReLU, shrinkage activation improves model performance on denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with well-preserved prediction performance. | https://proceedings.mlr.press/v139/zheng21c.html | https://proceedings.mlr.press/v139/zheng21c.html | https://proceedings.mlr.press/v139/zheng21c.html | http://proceedings.mlr.press/v139/zheng21c/zheng21c.pdf | ICML 2021 | |
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions | Zixin Zhong, Wang Chi Cheung, Vincent Tan | We consider a best arm identification (BAI) problem for stochastic bandits with adversarial corruptions in the fixed-budget setting of T steps. We design a novel randomized algorithm, Probabilistic Sequential Shrinking(u) (PSS(u)), which is agnostic to the amount of corruptions. When the amount of corruptions per step (CPS) is below a threshold, PSS(u) identifies the best arm or item with probability tending to 1 as T{\rightarrow}$\infty$. Otherwise, the optimality gap of the identified item degrades gracefully with the CPS.We argue that such a bifurcation is necessary. In PSS(u), the parameter u serves to balance between the optimality gap and success probability. The injection of randomization is shown to be essential to mitigate the impact of corruptions. To demonstrate this, we design two attack strategies that are applicable to any algorithm. We apply one of them to a deterministic analogue of PSS(u) known as Successive Halving (SH) by Karnin et al. (2013). The attack strategy results in a high failure probability for SH, but PSS(u) remains robust. In the absence of corruptions, PSS(2)’s performance guarantee matches SH’s. We show that when the CPS is sufficiently large, no algorithm can achieve a BAI probability tending to 1 as T{\rightarrow}$\infty$. Numerical experiments corroborate our theoretical findings. | https://proceedings.mlr.press/v139/zhong21a.html | https://proceedings.mlr.press/v139/zhong21a.html | https://proceedings.mlr.press/v139/zhong21a.html | http://proceedings.mlr.press/v139/zhong21a/zhong21a.pdf | ICML 2021 | |
Towards Distraction-Robust Active Visual Tracking | Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang | In active visual tracking, it is notoriously difficult when distracting objects appear, as distractors often mislead the tracker by occluding the target or bringing a confusing appearance. To address this issue, we propose a mixed cooperative-competitive multi-agent game, where a target and multiple distractors form a collaborative team to play against a tracker and make it fail to follow. Through learning in our game, diverse distracting behaviors of the distractors naturally emerge, thereby exposing the tracker’s weakness, which helps enhance the distraction-robustness of the tracker. For effective learning, we then present a bunch of practical methods, including a reward function for distractors, a cross-modal teacher-student learning strategy, and a recurrent attention mechanism for the tracker. The experimental results show that our tracker performs desired distraction-robust active visual tracking and can be well generalized to unseen environments. We also show that the multi-agent game can be used to adversarially test the robustness of trackers. | https://proceedings.mlr.press/v139/zhong21b.html | https://proceedings.mlr.press/v139/zhong21b.html | https://proceedings.mlr.press/v139/zhong21b.html | http://proceedings.mlr.press/v139/zhong21b/zhong21b.pdf | ICML 2021 | |
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping | Dongruo Zhou, Jiafan He, Quanquan Gu | Modern tasks in reinforcement learning have large state and action spaces. To deal with them efficiently, one often uses predefined feature mapping to represent states and actions in a low dimensional space. In this paper, we study reinforcement learning for discounted Markov Decision Processes (MDPs), where the transition kernel can be parameterized as a linear function of certain feature mapping. We propose a novel algorithm which makes use of the feature mapping and obtains a $\tilde O(d\sqrt{T}/(1-\gamma)^2)$ regret, where $d$ is the dimension of the feature space, $T$ is the time horizon and $\gamma$ is the discount factor of the MDP. To the best of our knowledge, this is the first polynomial regret bound without accessing a generative model or making strong assumptions such as ergodicity of the MDP. By constructing a special class of MDPs, we also show that for any algorithms, the regret is lower bounded by $\Omega(d\sqrt{T}/(1-\gamma)^{1.5})$. Our upper and lower bound results together suggest that the proposed reinforcement learning algorithm is near-optimal up to a $(1-\gamma)^{-0.5}$ factor. | https://proceedings.mlr.press/v139/zhou21a.html | https://proceedings.mlr.press/v139/zhou21a.html | https://proceedings.mlr.press/v139/zhou21a.html | http://proceedings.mlr.press/v139/zhou21a/zhou21a.pdf | ICML 2021 | |
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation | Aurick Zhou, Sergey Levine | While deep neural networks provide good performance for a range of challenging tasks, calibration and uncertainty estimation remain major challenges, especially under distribution shift. In this paper, we propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation, calibration, and out-of-distribution robustness with deep networks. Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle, but is computationally intractable to evaluate exactly for all but the simplest of model classes. We propose to use approximate Bayesian inference technqiues to produce a tractable approximation to the CNML distribution. Our approach can be combined with any approximate inference algorithm that provides tractable posterior densities over model parameters. We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration when faced with distribution shift. | https://proceedings.mlr.press/v139/zhou21b.html | https://proceedings.mlr.press/v139/zhou21b.html | https://proceedings.mlr.press/v139/zhou21b.html | http://proceedings.mlr.press/v139/zhou21b/zhou21b.pdf | ICML 2021 | |
Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations | Fan Zhou, Ping Li | Given $\bx_j = \btheta + \bepsilon_j$, $j=1,...,n$ where $\btheta \in \RR^d$ is an unknown parameter and $\bepsilon_j$ are i.i.d. Gaussian noise vectors, we study the estimation of $f(\btheta)$ for a given smooth function $f:\RR^d \rightarrow \RR$ equipped with an additive structure. We inherit the idea from a recent work which introduced an effective bias reduction technique through iterative bootstrap and derive a bias-reducing estimator. By establishing its normal approximation results, we show that the proposed estimator can achieve asymptotic normality with a looser constraint on smoothness compared with general smooth function due to the additive structure. Such results further imply that the proposed estimator is asymptotically efficient. Both upper and lower bounds on mean squared error are proved which shows the proposed estimator is minimax optimal for the smooth class considered. Numerical simulation results are presented to validate our analysis and show its superior performance of the proposed estimator over the plug-in approach in terms of bias reduction and building confidence intervals. | https://proceedings.mlr.press/v139/zhou21c.html | https://proceedings.mlr.press/v139/zhou21c.html | https://proceedings.mlr.press/v139/zhou21c.html | http://proceedings.mlr.press/v139/zhou21c/zhou21c.pdf | ICML 2021 | |
Incentivized Bandit Learning with Self-Reinforcing User Preferences | Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng | In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences. Besides addressing the tradeoff of exploration and exploitation, another key feature of this new MAB model is to balance reward and incentivizing payment. The goal of the agent is to maximize the total reward over a fixed time horizon $T$ with a low total payment. Our contributions in this paper are two-fold: (i) We propose a new MAB model with random arm selection that considers the relationship of users’ self-reinforcing preferences and incentives; and (ii) We leverage the properties of a multi-color Polya urn with nonlinear feedback model to propose two MAB policies termed "At-Least-$n$ Explore-Then-Commit" and "UCB-List". We prove that both policies achieve $O(log T)$ expected regret with $O(log T)$ expected payment over a time horizon $T$. We conduct numerical simulations to demonstrate and verify the performances of these two policies and study their robustness under various settings. | https://proceedings.mlr.press/v139/zhou21d.html | https://proceedings.mlr.press/v139/zhou21d.html | https://proceedings.mlr.press/v139/zhou21d.html | http://proceedings.mlr.press/v139/zhou21d/zhou21d.pdf | ICML 2021 | |
Towards Defending against Adversarial Examples via Attack-Invariant Features | Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao | Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously evolving attacks, models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples. To solve this problem, in this paper, we propose to remove adversarial noise by learning generalizable invariant features across attacks which maintain semantic classification information. Specifically, we introduce an adversarial feature learning mechanism to disentangle invariant features from adversarial noise. A normalization term has been proposed in the encoded space of the attack-invariant features to address the bias issue between the seen and unseen types of attacks. Empirical evaluations demonstrate that our method could provide better protection in comparison to previous state-of-the-art approaches, especially against unseen types of attacks and adaptive attacks. | https://proceedings.mlr.press/v139/zhou21e.html | https://proceedings.mlr.press/v139/zhou21e.html | https://proceedings.mlr.press/v139/zhou21e.html | http://proceedings.mlr.press/v139/zhou21e/zhou21e.pdf | ICML 2021 | |
Asymmetric Loss Functions for Learning with Noisy Labels | Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji | Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be robust to label noise. However, the symmetric condition is overly restrictive. In this work, we propose a new class of loss functions, namely asymmetric loss functions, which are robust to learning from noisy labels for arbitrary noise type. Subsequently, we investigate general theoretical properties of asymmetric loss functions, including classification-calibration, excess risk bound, and noise-tolerance. Meanwhile, we introduce the asymmetry ratio to measure the asymmetry of a loss function, and the empirical results show that a higher ratio will provide better robustness. Moreover, we modify several common loss functions, and establish the necessary and sufficient conditions for them to be asymmetric. Experiments on benchmark datasets demonstrate that asymmetric loss functions can outperform state-of-the-art methods. | https://proceedings.mlr.press/v139/zhou21f.html | https://proceedings.mlr.press/v139/zhou21f.html | https://proceedings.mlr.press/v139/zhou21f.html | http://proceedings.mlr.press/v139/zhou21f/zhou21f.pdf | ICML 2021 | |
Examining and Combating Spurious Features under Distribution Shift | Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig | A central goal of machine learning is to learn robust representations that capture the fundamental relationship between inputs and output labels. However, minimizing training errors over finite or biased datasets results in models latching on to spurious correlations between the training input/output pairs that are not fundamental to the problem at hand. In this paper, we define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution (i.e. covariate shift), models can still pick up spurious features from their training data. Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training losses over a set of pre-defined groups. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations that occur in the data. To address this, we further propose to minimize the worst-case losses over a more flexible set of distributions that are defined on the joint distribution of groups and instances, instead of treating each group as a whole at optimization time. Through extensive experiments on one image and two language tasks, we show that our model is significantly more robust than comparable baselines under various partitions. | https://proceedings.mlr.press/v139/zhou21g.html | https://proceedings.mlr.press/v139/zhou21g.html | https://proceedings.mlr.press/v139/zhou21g.html | http://proceedings.mlr.press/v139/zhou21g/zhou21g.pdf | ICML 2021 | |
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm | Mingkang Zhu, Tianlong Chen, Zhangyang Wang | Sparse adversarial attacks can fool deep neural networks (DNNs) by only perturbing a few pixels (regularized by $\ell_0$ norm). Recent efforts combine it with another $\ell_\infty$ imperceptible on the perturbation magnitudes. The resultant sparse and imperceptible attacks are practically relevant, and indicate an even higher vulnerability of DNNs that we usually imagined. However, such attacks are more challenging to generate due to the optimization difficulty by coupling the $\ell_0$ regularizer and box constraints with a non-convex objective. In this paper, we address this challenge by proposing a homotopy algorithm, to jointly tackle the sparsity and the perturbation bound in one unified framework. Each iteration, the main step of our algorithm is to optimize an $\ell_0$-regularized adversarial loss, by leveraging the nonmonotone Accelerated Proximal Gradient Method (nmAPG) for nonconvex programming; it is followed by an $\ell_0$ change control step, and an optional post-attack step designed to escape bad local minima. We also extend the algorithm to handling the structural sparsity regularizer. We extensively examine the effectiveness of our proposed \textbf{homotopy attack} for both targeted and non-targeted attack scenarios, on CIFAR-10 and ImageNet datasets. Compared to state-of-the-art methods, our homotopy attack leads to significantly fewer perturbations, e.g., reducing 42.91% on CIFAR-10 and 75.03% on ImageNet (average case, targeted attack), at similar maximal perturbation magnitudes, when still achieving 100% attack success rates. Our codes are available at: {\small\url{https://github.com/VITA-Group/SparseADV_Homotopy}}. | https://proceedings.mlr.press/v139/zhu21a.html | https://proceedings.mlr.press/v139/zhu21a.html | https://proceedings.mlr.press/v139/zhu21a.html | http://proceedings.mlr.press/v139/zhu21a/zhu21a.pdf | ICML 2021 | |
Data-Free Knowledge Distillation for Heterogeneous Federated Learning | Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou | Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which can incur drifted global models that are slow to converge. Knowledge Distillation has recently emerged to tackle this issue, by refining the server model using aggregated knowledge from heterogeneous users, other than directly averaging their model parameters. This approach, however, depends on a proxy dataset, making it impractical unless such a prerequisite is satisfied. Moreover, the ensemble knowledge is not fully utilized to guide local model learning, which may in turn affect the quality of the aggregated model. Inspired by the prior art, we propose a data-free knowledge distillation approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies powered by theoretical implications show that our approach facilitates FL with better generalization performance using fewer communication rounds, compared with the state-of-the-art. | https://proceedings.mlr.press/v139/zhu21b.html | https://proceedings.mlr.press/v139/zhu21b.html | https://proceedings.mlr.press/v139/zhu21b.html | http://proceedings.mlr.press/v139/zhu21b/zhu21b.pdf | ICML 2021 | |
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs | Chunjiang Zhu, Qinqing Liu, Jinbo Bi | Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over graphs. As real-world networks are becoming very large and naturally distributed, distributed graph sparsification has drawn considerable attention. In this work, we design communication-efficient distributed algorithms for constructing spectral vertex sparsifiers, which closely preserve effective resistance distances on a subset of vertices of interest in the original graphs, under the well-established message passing communication model. We prove that the communication cost approximates the lower bound with only a small gap. We further provide algorithms for constructing pair-wise spanners which approximate the shortest distances between each pair of vertices in a target set, instead of all pairs, and incur communication costs that are much smaller than those of existing algorithms in the message passing model. Experiments are performed to validate the communication efficiency of the proposed algorithms under the guarantee that the constructed sparsifiers have a good approximation quality. | https://proceedings.mlr.press/v139/zhu21c.html | https://proceedings.mlr.press/v139/zhu21c.html | https://proceedings.mlr.press/v139/zhu21c.html | http://proceedings.mlr.press/v139/zhu21c/zhu21c.pdf | ICML 2021 | |
Few-shot Language Coordination by Modeling Theory of Mind | Hao Zhu, Graham Neubig, Yonatan Bisk | No man is an island. Humans develop the ability to communicate with a large community by coordinating with different interlocutors within short conversations. This ability is largely understudied by the research on building neural language communicative agents. We study the task of few-shot language coordination: agents quickly adapting to their conversational partners’ language abilities. Different from current communicative agents trained with self-play, we in- investigate this more general paradigm by requiring the lead agent to coordinate with a population of agents each of whom has different linguistic abilities. This leads to a general agent able to quickly adapt to communicating with unseen agents in the population. Unlike prior work, success here requires the ability to model the partner’s beliefs, a vital component of human communication. Drawing inspiration from the study of theory-of-mind (ToM; Premack & Woodruff (1978)), we study the effect of the speaker explicitly modeling the listener’s mental state. Learning by communicating with a population, the speakers, as shown in our experiments, acquire the ability to learn to predict the reactions of their partner upon various messages on-the-fly. The speaker’s predictions for the future actions help it generate the best instructions in order to maximize communicative goal with message costs. To examine our hypothesis that the instructions generated with ToM modeling yield better communication per- performance, we employ our agents in both a referential game and a language navigation task. Positive results from our experiments also hint at the importance of explicitly modeling language acquisition as a socio-pragmatic progress. | https://proceedings.mlr.press/v139/zhu21d.html | https://proceedings.mlr.press/v139/zhu21d.html | https://proceedings.mlr.press/v139/zhu21d.html | http://proceedings.mlr.press/v139/zhu21d/zhu21d.pdf | ICML 2021 | |
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels | Zhaowei Zhu, Yiwen Song, Yang Liu | The label noise transition matrix, characterizing the probabilities of a training instance being wrongly annotated, is crucial to designing popular solutions to learning with noisy labels. Existing works heavily rely on finding “anchor points” or their approximates, defined as instances belonging to a particular class almost surely. Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points. In this paper, we propose an alternative option to the above task. Our main contribution is the discovery of an efficient estimation procedure based on a clusterability condition. We prove that with clusterable representations of features, using up to third-order consensuses of noisy labels among neighbor representations is sufficient to estimate a unique transition matrix. Compared with methods using anchor points, our approach uses substantially more instances and benefits from a much better sample complexity. We demonstrate the estimation accuracy and advantages of our estimates using both synthetic noisy labels (on CIFAR-10/100) and real human-level noisy labels (on Clothing1M and our self-collected human-annotated CIFAR-10). Our code and human-level noisy CIFAR-10 labels are available at https://github.com/UCSC-REAL/HOC. | https://proceedings.mlr.press/v139/zhu21e.html | https://proceedings.mlr.press/v139/zhu21e.html | https://proceedings.mlr.press/v139/zhu21e.html | http://proceedings.mlr.press/v139/zhu21e/zhu21e.pdf | ICML 2021 | |
Commutative Lie Group VAE for Disentanglement Learning | Xinqi Zhu, Chang Xu, Dacheng Tao | We view disentanglement learning as discovering an underlying structure that equivariantly reflects the factorized variations shown in data. Traditionally, such a structure is fixed to be a vector space with data variations represented by translations along individual latent dimensions. We argue this simple structure is suboptimal since it requires the model to learn to discard the properties (e.g. different scales of changes, different levels of abstractness) of data variations, which is an extra work than equivariance learning. Instead, we propose to encode the data variations with groups, a structure not only can equivariantly represent variations, but can also be adaptively optimized to preserve the properties of data variations. Considering it is hard to conduct training on group structures, we focus on Lie groups and adopt a parameterization using Lie algebra. Based on the parameterization, some disentanglement learning constraints are naturally derived. A simple model named Commutative Lie Group VAE is introduced to realize the group-based disentanglement learning. Experiments show that our model can effectively learn disentangled representations without supervision, and can achieve state-of-the-art performance without extra constraints. | https://proceedings.mlr.press/v139/zhu21f.html | https://proceedings.mlr.press/v139/zhu21f.html | https://proceedings.mlr.press/v139/zhu21f.html | http://proceedings.mlr.press/v139/zhu21f/zhu21f.pdf | ICML 2021 | |
Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks | Huiping Zhuang, Zhenyu Weng, Fulin Luo, Toh Kar-Ann, Haizhou Li, Zhiping Lin | Gradient staleness is a major side effect in decoupled learning when training convolutional neural networks asynchronously. Existing methods that ignore this effect might result in reduced generalization and even divergence. In this paper, we propose an accumulated decoupled learning (ADL), which includes a module-wise gradient accumulation in order to mitigate the gradient staleness. Unlike prior arts ignoring the gradient staleness, we quantify the staleness in such a way that its mitigation can be quantitatively visualized. As a new learning scheme, the proposed ADL is theoretically shown to converge to critical points in spite of its asynchronism. Extensive experiments on CIFAR-10 and ImageNet datasets are conducted, demonstrating that ADL gives promising generalization results while the state-of-the-art methods experience reduced generalization and divergence. In addition, our ADL is shown to have the fastest training speed among the compared methods. | https://proceedings.mlr.press/v139/zhuang21a.html | https://proceedings.mlr.press/v139/zhuang21a.html | https://proceedings.mlr.press/v139/zhuang21a.html | http://proceedings.mlr.press/v139/zhuang21a/zhuang21a.pdf | ICML 2021 | |
Demystifying Inductive Biases for (Beta-)VAE Based Architectures | Dominik Zietlow, Michal Rolinek, Georg Martius | The performance of Beta-Variational-Autoencoders and their variants on learning semantically meaningful, disentangled representations is unparalleled. On the other hand, there are theoretical arguments suggesting the impossibility of unsupervised disentanglement. In this work, we shed light on the inductive bias responsible for the success of VAE-based architectures. We show that in classical datasets the structure of variance, induced by the generating factors, is conveniently aligned with the latent directions fostered by the VAE objective. This builds the pivotal bias on which the disentangling abilities of VAEs rely. By small, elaborate perturbations of existing datasets, we hide the convenient correlation structure that is easily exploited by a variety of architectures. To demonstrate this, we construct modified versions of standard datasets in which (i) the generative factors are perfectly preserved; (ii) each image undergoes a mild transformation causing a small change of variance; (iii) the leading VAE-based disentanglement architectures fail to produce disentangled representations whilst the performance of a non-variational method remains unchanged. | https://proceedings.mlr.press/v139/zietlow21a.html | https://proceedings.mlr.press/v139/zietlow21a.html | https://proceedings.mlr.press/v139/zietlow21a.html | http://proceedings.mlr.press/v139/zietlow21a/zietlow21a.pdf | ICML 2021 | |
Recovering AES Keys with a Deep Cold Boot Attack | Itamar Zimerman, Eliya Nachmani, Lior Wolf | Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. While most of the bits have been corrupted, many bits, at random locations, have not. Since the keys in many encryption schemes are being expanded in memory into longer keys with fixed redundancies, the keys can often be restored. In this work we combine a deep error correcting code technique together with a modified SAT solver scheme in order to apply the attack to AES keys. Even though AES consists Rijndael SBOX elements, that are specifically designed to be resistant to linear and differential cryptanalysis, our method provides a novel formalization of the AES key scheduling as a computational graph, which is implemented by neural message passing network. Our results show that our methods outperform the state of the art attack methods by a very large gap. | https://proceedings.mlr.press/v139/zimerman21a.html | https://proceedings.mlr.press/v139/zimerman21a.html | https://proceedings.mlr.press/v139/zimerman21a.html | http://proceedings.mlr.press/v139/zimerman21a/zimerman21a.pdf | ICML 2021 | |
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning | Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng | We consider the problem of learning fair policies in (deep) cooperative multi-agent reinforcement learning (MARL). We formalize it in a principled way as the problem of optimizing a welfare function that explicitly encodes two important aspects of fairness: efficiency and equity. We provide a theoretical analysis of the convergence of policy gradient for this problem. As a solution method, we propose a novel neural network architecture, which is composed of two sub-networks specifically designed for taking into account these two aspects of fairness. In experiments, we demonstrate the importance of the two sub-networks for fair optimization. Our overall approach is general as it can accommodate any (sub)differentiable welfare function. Therefore, it is compatible with various notions of fairness that have been proposed in the literature (e.g., lexicographic maximin, generalized Gini social welfare function, proportional fairness). Our method is generic and can be implemented in various MARL settings: centralized training and decentralized execution, or fully decentralized. Finally, we experimentally validate our approach in various domains and show that it can perform much better than previous methods, both in terms of efficiency and equity. | https://proceedings.mlr.press/v139/zimmer21a.html | https://proceedings.mlr.press/v139/zimmer21a.html | https://proceedings.mlr.press/v139/zimmer21a.html | http://proceedings.mlr.press/v139/zimmer21a/zimmer21a.pdf | ICML 2021 | |
Contrastive Learning Inverts the Data Generating Process | Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel | Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feedforward models trained with objectives belonging to the commonly used InfoNCE family learn to implicitly invert the underlying generative model of the observed data. While the proofs make certain statistical assumptions about the generative model, we observe empirically that our findings hold even if these assumptions are severely violated. Our theory highlights a fundamental connection between contrastive learning, generative modeling, and nonlinear independent component analysis, thereby furthering our understanding of the learned representations as well as providing a theoretical foundation to derive more effective contrastive losses. | https://proceedings.mlr.press/v139/zimmermann21a.html | https://proceedings.mlr.press/v139/zimmermann21a.html | https://proceedings.mlr.press/v139/zimmermann21a.html | http://proceedings.mlr.press/v139/zimmermann21a/zimmermann21a.pdf | ICML 2021 | |
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning | Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson | To rapidly learn a new task, it is often essential for agents to explore efficiently - especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent’s task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods. | https://proceedings.mlr.press/v139/zintgraf21a.html | https://proceedings.mlr.press/v139/zintgraf21a.html | https://proceedings.mlr.press/v139/zintgraf21a.html | http://proceedings.mlr.press/v139/zintgraf21a/zintgraf21a.pdf | ICML 2021 | |
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise | Difan Zou, Spencer Frei, Quanquan Gu | We analyze the properties of adversarial training for learning adversarially robust halfspaces in the presence of agnostic label noise. Denoting $\mathsf{OPT}_{p,r}$ as the best classification error achieved by a halfspace that is robust to perturbations of $\ell^{p}$ balls of radius $r$, we show that adversarial training on the standard binary cross-entropy loss yields adversarially robust halfspaces up to classification error $\tilde O(\sqrt{\mathsf{OPT}_{2,r}})$ for $p=2$, and $\tilde O(d^{1/4} \sqrt{\mathsf{OPT}_{\infty, r}})$ when $p=\infty$. Our results hold for distributions satisfying anti-concentration properties enjoyed by log-concave isotropic distributions among others. We additionally show that if one instead uses a non-convex sigmoidal loss, adversarial training yields halfspaces with an improved robust classification error of $O(\mathsf{OPT}_{2,r})$ for $p=2$, and $O(d^{1/4} \mathsf{OPT}_{\infty, r})$ when $p=\infty$. To the best of our knowledge, this is the first work showing that adversarial training provably yields robust classifiers in the presence of noise. | https://proceedings.mlr.press/v139/zou21a.html | https://proceedings.mlr.press/v139/zou21a.html | https://proceedings.mlr.press/v139/zou21a.html | http://proceedings.mlr.press/v139/zou21a/zou21a.pdf | ICML 2021 | |
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients | Difan Zou, Quanquan Gu | Hamiltonian Monte Carlo (HMC), built based on the Hamilton’s equation, has been witnessed great success in sampling from high-dimensional posterior distributions. However, it also suffers from computational inefficiency, especially for large training datasets. One common idea to overcome this computational bottleneck is using stochastic gradients, which only queries a mini-batch of training data in each iteration. However, unlike the extensive studies on the convergence analysis of HMC using full gradients, few works focus on establishing the convergence guarantees of stochastic gradient HMC algorithms. In this paper, we propose a general framework for proving the convergence rate of HMC with stochastic gradient estimators, for sampling from strongly log-concave and log-smooth target distributions. We show that the convergence to the target distribution in $2$-Wasserstein distance can be guaranteed as long as the stochastic gradient estimator is unbiased and its variance is upper bounded along the algorithm trajectory. We further apply the proposed framework to analyze the convergence rates of HMC with four standard stochastic gradient estimators: mini-batch stochastic gradient (SG), stochastic variance reduced gradient (SVRG), stochastic average gradient (SAGA), and control variate gradient (CVG). Theoretical results explain the inefficiency of mini-batch SG, and suggest that SVRG and SAGA perform better in the tasks with high-precision requirements, while CVG performs better for large dataset. Experiment results verify our theoretical findings. | https://proceedings.mlr.press/v139/zou21b.html | https://proceedings.mlr.press/v139/zou21b.html | https://proceedings.mlr.press/v139/zou21b.html | http://proceedings.mlr.press/v139/zou21b/zou21b.pdf | ICML 2021 | |
A Functional Perspective on Learning Symmetric Functions with Neural Networks | Aaron Zweig, Joan Bruna | Symmetric functions, which take as input an unordered, fixed-size set, are known to be universally representable by neural networks that enforce permutation invariance. These architectures only give guarantees for fixed input sizes, yet in many practical applications, including point clouds and particle physics, a relevant notion of generalization should include varying the input size. In this work we treat symmetric functions (of any size) as functions over probability measures, and study the learning and representation of neural networks defined on measures. By focusing on shallow architectures, we establish approximation and generalization bounds under different choices of regularization (such as RKHS and variation norms), that capture a hierarchy of functional spaces with increasing degree of non-linear learning. The resulting models can be learned efficiently and enjoy generalization guarantees that extend across input sizes, as we verify empirically. | https://proceedings.mlr.press/v139/zweig21a.html | https://proceedings.mlr.press/v139/zweig21a.html | https://proceedings.mlr.press/v139/zweig21a.html | http://proceedings.mlr.press/v139/zweig21a/zweig21a.pdf | ICML 2021 |
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