pwc-archive/papers-with-abstracts huggingface dataset:
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
Authors: Pengzhou Abel Wu, Kenji Fukumizu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Causal Inference

As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain features belong to a single treatment group. We use a latent variable to model a prognostic score which is widely used in biostatistics and sufficient for TEs; i.e., we build a generative prognostic model. We prove that the latent variable recovers a prognostic score, and the model identifies individualized treatment effects. The model is then learned as $\beta$-Intact-VAE––a new type of variational autoencoder (VAE). We derive the TE error bounds that enable representations balanced for treatment groups conditioned on individualized features. The proposed method is compared with recent methods using (semi-)synthetic datasets.
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Authors: Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Federated Learning, image-classification, Image Classification

Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice. The clients available for training are observed to have periodically shifting distributions changing with the time of day, which can cause instability in training and degrade the model performance. In this paper, instead of modeling the distribution shift with a block-cyclic pattern as previous works, we model it with a mixture of distributions that gradually changes between daytime modes and nighttime modes, and find this intuitive model to better match the observations in practical federated learning systems. We propose a Federated Expectation-Maximization algorithm enhanced by Temporal priors of the shifting distribution (FedTEM), which jointly learns a mixture model to infer the mode of each client, while training a network with multiple light-weight branches specializing at different modes. Experiments for image classification on EMNIST and CIFAR datasets, and next word prediction on the Stack Overflow dataset show that the proposed algorithm can effectively mitigate the impact of the distribution shift and significantly improve the final model performance.
Wasserstein Weisfeiler-Lehman Subtree Distance for Graph-Structured Data
Authors: Zhongxi Fang, Jianming Huang, Hiroyuki Kasai
Date: 2021-09-29 00:00:00
Tasks: Graph Classification, valid

Defining a valid graph distance is a challenging task in graph machine learning because we need to consider the theoretical validity of the distance, its computational complexity, and effectiveness as a distance between graphs. Addressing the shortcomings of the popular Weisfeiler-Lehman (WL) test for the graph isomorphism problem, this paper proposes a novel distance between graph structures. More specifically, we first analyze the WL algorithm from a geometric point of view and argue that discriminating nodes based on only the consistency of categorical labels do not fully capture important structural information. Therefore, instead of using such categorical labels, we define a node distance between WL subtrees with tree edit distance and propose an efficient calculation algorithm. We then apply the proposed node distance to define a graph Wasserstein distance on tree edit embedding space exploiting Optimal Transport framework. To summarize, these two distances have been proposed at the node level and graph level, respectively. Numerical experimentations on graph classification tasks show that the proposed graph Wasserstein distance performs equally or better than conventional methods.
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
Authors: Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh
Date: 2021-09-29 00:00:00
Tasks: Atari Games, Inductive Bias, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study different notions of equivariance as an inductive bias in Reinforcement Learning (RL) and propose new mechanisms for recovering representations that are equivariant to both an agent’s action, and symmetry transformations of the state-action pairs. Whereas prior work on exploiting symmetries in deep RL can only incorporate predefined linear transformations, our approach allows for non-linear symmetry transformations of state-action pairs to be learned from the data itself. This is achieved through an equivariant Lie algebraic parameterization of state and action encodings, equivariant latent transition models, and the use of symmetry-based losses. We demonstrate the advantages of our learned equivariant representations for Atari games, in a data-efficient setting limited to 100k steps of interactions with the environment. Our method, which we call Equivariant representations for RL (EqR), outperforms many previous methods in a similar setting by achieving a median human-normalized score of 0.418, and surpassing human-level performance on 8 out of the 26 games.
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
Authors: Moise Blanchard, Mohammed Amine Bennouna
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

In this paper, we analyze the number of neurons and training parameters that a neural network needs to approximate multivariate functions of bounded second mixed derivatives --- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, drastically lessening the curse of dimensionality. Our bounds hold for general activation functions, including ReLU. We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Korobov functions, showing that neural networks are near-optimal function approximators.
Adam is no better than normalized SGD: Dissecting how adaptivity improves GAN performance
Authors: Samy Jelassi, Arthur Mensch, Gauthier Gidel, Yuanzhi Li
Date: 2021-09-29 00:00:00

 Adaptive methods are widely used for training generative adversarial networks (GAN). While there has been some work to pinpoint the marginal value of adaptive methods in minimization problems, it remains unclear why it is still the method of choice for GAN training. This paper formally studies how adaptive methods help performance in GANs.  First, we dissect Adam---the most popular adaptive method for GAN training---by comparing with SGDA the direction and the norm of its update vector. We empirically show that SGDA with the same vector norm as Adam reaches similar or even better performance than the latter. This empirical study encourages us to consider normalized stochastic gradient descent ascent (nSGDA) as a simpler alternative to Adam. We then propose a synthetic theoretical framework to understand why nSGDA yields better performance than SGDA for GANs.  In that situation, we prove that a GAN trained with nSGDA provably recovers all the modes of the true distribution. In contrast, the same networks trained with SGDA (and any learning rate configuration) suffers from mode collapsing. The critical insight in our analysis is that normalizing the gradients forces the discriminator and generator to update at the same pace. We  empirically show the competitive performance of nSGDA on real-world datasets.
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representation
Authors: Ji Xin, Chenyan Xiong, Ashwin Srinivasan, Ankita Sharma, Damien Jose, Paul N. Bennett
Date: 2021-09-29 00:00:00
Tasks: Representation Learning, Retrieval, Text Retrieval

Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search. This requires strong locality properties from the representation space, i.e, the close allocations of each small group of relevant texts, which is hard to generalize to domains without sufficient training data. In this paper, we aim to improve the generalization ability of DR models from source training domains with rich supervision signals to target domains without any relevant labels, in the zero-shot setting. To achieve that, we propose Momentum adversarial Domain Invariant Representation learning (MoDIR), which introduces a momentum method in the DR training process to train a domain classifier on the source versus target, and then adversarially updates the DR encoder to learn domain invariant representations. Our experiments show that MoDIR robustly outperforms its baselines on 10+ ranking datasets from the BEIR benchmark in the zero-shot setup, with more than 10% relative gains on datasets where the evaluation of DR models is sensitive enough. Source code of this paper will be released.
A First-Order Method for Estimating Natural Gradients for Variational Inference with Gaussians and Gaussian Mixture Models
Authors: Oleg Arenz, Zihan Ye, Philipp Dahlinger, Gerhard Neumann
Date: 2021-09-29 00:00:00
Tasks: Variational Inference

Variational inference with full-covariance Gaussian approximations is an important line of research, as such Gaussian variational approximations (GVAs) allow for tractable approximate inference while yielding superior approximations compared to mean-field methods. Moreover, it was recently shown, that the problem of variational inference with Gaussian mixture models can be reduced to Gaussian variational inference using VIPS, which is a procedure similar to expectation maximization. Effective approaches for Gaussian variational inference are MORE, VOGN, and VON, which are zero-order, first-order, and second-order, respectively. We focus on the first-order setting, which is arguably the most relevant for variational inference, and show that the biases added by the generalized Gauß-Newton approximation, which is applied by VOGN, can seriously affect the quality of the learned approximation. Hence, we propose gMORE, a method that is similar to MORE but differs by incorporating gradient information. GradientMORE achieves unbiased high-quality approximations of the Hessian that are similar to VON which has direct access to the Hessian. Our algorithm converges even in settings where VOGN does not converge. Compared to MORE, the additional information improves sample efficiency by about an order of magnitude. Furthermore, we evaluate the different approaches in the GMM setting by modifying VIPS, which has previously only been tested in combination with MORE, and show that the results from the GVA setting are transferable to GMMs, setting a new standard for GMM-based variational inference.
Teamwork makes von Neumann work:Min-Max Optimization in Two-Team Zero-Sum Games
Authors: Fivos Kalogiannis, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis
Date: 2021-09-29 00:00:00

Motivated by recent advances in both theoretical and applied aspects of multiplayer games, spanning from e-sports to multi-agent generative adversarial networks, we focus on min-max optimization in team zero-sum games. In this class of games, players are split in two teams with payoffs equal within the same team and of opposite sign across the opponent team. Unlike the textbook two player zero-sum games, finding a Nash equilibrium in our class can be shown to be $\textsf{CLS}$-hard, i.e., it is unlikely to have a polynomial time algorithm for computing Nash equilibria. Moreover In this generalized framework, we establish that even asymptotic last iterate or time average convergence to a Nash Equilibrium is not possible using Gradient Descent Ascent (GDA), its optimistic variant and extra gradient. Specifically, we present a family of team games whose induced utility is non-multilinear with non-attractive $\textit{per-se}$ mixed Nash Equilibria, as strict saddle points of the underlying optimization landscape. Leveraging techniques from control theory, we complement these negative results by designing a modified GDA that converges locally to Nash equilibria. Finally, we discuss connections of our framework with AI architectures with team competition structure like multi-agent generative adversarial networks.
Reinforcement Learning with Predictive Consistent Representations
Authors: Tao Huang, Xiao Chen, Jiachen Wang
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning informative representations from image-based observations is a fundamental problem in deep Reinforcement Learning (RL). However, data inefficiency remains a significant barrier. To this end, we investigate Predictive Consistent Representations (PCR) that enforces predictive consistency on a learned dynamic model. Unlike previous algorithms that simply exploit a forward dynamics model, the PCR agent is trained to predict the future state and retain consistency across the predicted state of observation and its multiple views, which is demonstrated through careful ablation experiments. We empirically show that PCR outperforms the current state-of-the-art baselines in terms of data efficiency on a series of pixel-based control tasks in the DeepMind control suite. Notably, on challenging tasks like Cheetah-run, PCR reaches a 47.4% improvement when environmental steps are limited to 100k steps.
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search
Authors: Hyeonmin Ha, Ji-Hoon Kim, Semin Park, Byung-Gon Chun
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Computational Efficiency, Meta-Learning, Neural Architecture Search

One-shot Neural Architecture Search (NAS) usually constructs an over-parameterized network, which we call a supernet, and typically adopts sharing parameters among the sub-models to improve computational efficiency. One-shot NAS often repeatedly samples sub-models from the supernet and trains them to optimize the shared parameters. However, this training strategy suffers from multi-model forgetting. Training a sampled sub-model overrides the previous knowledge learned by the other sub-models, resulting in an unfair performance evaluation between the sub-models. We propose Supernet with Unbiased Meta-Features for Neural Architecture Search (SUMNAS), a supernet learning strategy based on meta-learning to tackle the knowledge forgetting issue. During the training phase, we explicitly address the multi-model forgetting problem and help the supernet learn unbiased meta-features, independent from the sampled sub-models. Once training is over, sub-models can be instantly compared to get the overall ranking or the best sub-model. Our evaluation on the NAS-Bench-201 and MobileNet-based search space demonstrate that SUMNAS shows improved ranking ability and finds architectures whose performance is on par with existing state-of-the-art NAS algorithms.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising
Authors: Il Yong Chun, Dongwon Park, Xuehang Zheng, Se Young Chun, Yong Long
Date: 2021-09-29 00:00:00
Tasks: Denoising, Image Denoising, regression, Self-Supervised Learning

Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies. Yet, studying and understanding self-supervised learning for regression tasks -- except for a particular regression task, image denoising -- have lagged behind. This paper proposes a general self-supervised regression learning (SSRL) framework that enables learning regression neural networks with only input data (but without ground-truth target data), by using a designable operator that encapsulates domain knowledge of a specific application. The paper underlines the importance of domain knowledge by showing that under some mild conditions, the better designable operator is used, the proposed SSRL loss becomes closer to ordinary supervised learning loss. Numerical experiments for natural image denoising and low-dose computational tomography denoising demonstrate that proposed SSRL significantly improves the denoising quality over several existing self-supervised denoising methods.
Adversarial Robustness via Adaptive Label Smoothing
Authors: Qibing Ren, Liangliang Shi, Lanjun Wang, Junchi Yan
Date: 2021-09-29 00:00:00
Tasks: Adversarial Robustness

Adversarial training (AT) has become a dominant defense paradigm by enforcing the model's predictions to be locally invariant to adversarial examples. Being a simple technique, Label smoothing (LS) has shown its potential for improving model robustness. However, the prior study shows the benefit of directly combining two techniques together is limited. In this paper, we aim to better understand the behavior of LS and explore new algorithms for more effective LS on improving adversarial robustness. We first show both theoretically and empirically that strong smoothing in AT increases local smoothness of the loss surface which is beneficial for robustness but sacrifices the training loss which influences the accuracy of samples near the decision boundary. Based on this result, we propose \textit{surface smoothing adversarial training} (SSAT). Specifically, much stronger smoothness is used on the perturbed examples farther away from the decision boundary to achieve better robustness, while weaker smoothness is on those closer to the decision boundary to avoid incorrect classification on clean samples. Meanwhile, LS builds a different representation space among data classes in which SSAT differs from other AT methods. We study such a distinction and further propose a cooperative defense strategy termed by Co-SSAT. Experimental results show that our Co-SSAT achieves the state-of-the-art performances on CIFAR-10 with $\ell_{\infty}$ adversaries and also has a good generalization ability of unseen attacks, i.e., other $\ell_p$ norms, or larger perturbations due to the smoothness property of the loss surface.
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
Authors: Ronghang Zhu, Sheng Li
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Data Augmentation, Domain Generalization

Single domain generalization (SDG) is a challenging scenario of domain generalization, where only one source domain is available to train the model. Typical SDG methods are based on the adversarial data augmentation strategy, which complements the diversity of source domain to learn a robust model. Existing SDG methods require the source and target domains to have the same label space. However, as target domains may contain novel categories unseen in source label space, this assumption is not practical in many real-world applications. In this paper, we propose a challenging and untouched problem: \textit{Open-Set Single Domain Generalization} (OS-SDG), where target domains include unseen categories out of source label space. The goal of OS-SDG is to learn a model, with only one source domain, to classify a target sample with correct class if it belongs to source label space, or assign it to unknown classes. We design a \textit{CrossMatch} approach to improve the performance of SDG methods on identifying unknown classes by leveraging a multi-binary classifier. CrossMatch generates auxiliary samples out of source label space by using an adversarial data augmentation strategy. We also adopt a consistency regularization on generated auxiliary samples between multi-binary classifiers and the model trained by SDG methods, to improve the model’s capability on unknown class identification. Experimental results on benchmark datasets prove the effectiveness of CrossMatch on enhancing the performance of SDG methods in the OS-SDG setting.
Deep banach space kernels
Authors: Mrityunjay Bhardwaj
Date: 2021-09-29 00:00:00
Tasks: Gaussian Processes

The recent success of deep learning has encouraged many researchers to explore the deep/concatenated variants of classical kernel methods. Some of which includes MLMKL, DGP and DKL. Although, These methods have proven to be quite useful in various real-world settings. They still suffer from the limitations of only utilizing kernels from Hilbert spaces. In this paper, we address these shortcomings by introducing a new class of concatenated kernel learning methods that use the kernels from the reproducing kernel Banach spaces(RKBSs). These spaces turned out to be one of the most general spaces where a reproducing Kernel exists. We propose a framework of construction for these Deep RKBS models and then provide a representer theorem for regularized learning problems. We also describe the relationship with its deep RKHS variant as well as standard Deep Gaussian Processes. In the end, we construct and implement a two-layer deep RKBS model and demonstrate it on a range of machine learning tasks.
Why do embedding spaces look as they do?
Authors: Xingzhi Guo, Baojian Zhou, Haochen Chen, Sergiy Verstyuk, Steven Skiena
Date: 2021-09-29 00:00:00

The power of embedding representations is a curious phenomenon.   For embeddings to work effectively as feature representations, there must exist substantial latent structure inherent in the domain to be encoded.  Language vocabularies and Wikipedia topics are human-generated structures that reflect how people organize their world, and what they find important. The structure of the resulting embedding spaces reflects the human evolution of language formation and the cultural processes shaping our world.

This paper studies what the observed structure of embeddings can tell us about the natural processes that generate new knowledge or concepts.   We demonstrate that word and graph embeddings trained on standard datasets using several popular algorithms consistently share two distinct properties: (1) a decreasing neighbor frequency concentration with rank, and (2) specific clustering velocities and power-law based community structures.
We then assess a variety of generative models of embedding spaces by these criteria, and conclude that incremental insertion processes based on the Barabási-Albert network generation process best model the observed phenomenon on language and network data.
Offline-Online Reinforcement Learning: Extending Batch and Online RL
Authors: Maryam Hashemzadeh, Wesley Chung, Martha White
Date: 2021-09-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Batch RL has seen a surge in popularity and is applicable in many practical scenarios where past data is available. Unfortunately, the performance of batch RL agents is limited in both theory and practice without strong assumptions on the data-collection process e.g. sufficient coverage or a good policy. To enable better performance, we investigate the offline-online setting: The agent has access to a batch of data to train on but is also allowed to learn during the evaluation phase in an online manner. This is an extension to batch RL, allowing the agent to adapt to new situations without having to precommit to a policy. In our experiments, we find that agents trained in an offline-online manner can outperform agents trained only offline or online, sometimes by a large margin, for different dataset sizes and data-collection policies. Furthermore, we investigate the use of optimism vs. pessimism for value functions in the offline-online setting due to their use in batch and online RL.