pwc-archive/papers-with-abstracts huggingface dataset:
Intriguing class-wise properties of adversarial training
Authors: Qi Tian, Kun Kuang, Fei Wu, Yisen Wang
Date: 2021-01-01 00:00:00
Tasks: Adversarial Robustness

Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples. However, previous works mainly focus on the overall robustness of the model, and the in-depth analysis on the role of each class involved in adversarial training is still missing. In this paper, we provide the first detailed class-wise diagnosis of adversarial training on six widely used datasets, $\textit{i.e.}$, MNIST, CIFAR-10, CIFAR-100, SVHN, STL-10 and ImageNet. Surprisingly, we find that there are $\textit{remarkable robustness discrepancies among classes}$, demonstrating the following intriguing properties: 1) Many examples from a certain class could only be maliciously attacked to some specific semantic-similar classes, and these examples will not exist adversarial counterparts in bounded $\epsilon$-ball if we re-train the model without those specific classes; 2) The robustness of each class is positively correlated with its norm of classifier weight in deep neural networks; 3) Stronger attacks are usually more powerful for vulnerable classes, and we empirically propose a simple but effective attack to further verify these vulnerable classes are major hidden dangers of the robust model. We believe these findings can contribute to a more comprehensive understanding of adversarial training as well as further improvement of adversarial robustness.
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Authors: Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Fairness, Link Prediction

Disparate impact has raised serious concerns in machine learning applications and its societal impacts. In response to the need of mitigating discrimination, fairness has been regarded as a crucial property in algorithmic design. In this work, we study the problem of disparate impact on graph-structured data. Specifically, we focus on dyadic fairness, which articulates a fairness concept that a predictive relationship between two instances should be independent of the sensitive attributes. Based on this, we theoretically relate the graph connections to dyadic fairness on link predictive scores in learning graph neural networks, and reveal that regulating weights on existing edges in a graph contributes to dyadic fairness conditionally. Subsequently, we propose our algorithm, \textbf{FairAdj}, to empirically learn a fair adjacency matrix with proper graph structural constraints for fair link prediction, and in the meanwhile preserve predictive accuracy as much as possible. Empirical validation demonstrates that our method delivers effective dyadic fairness in terms of various statistics, and at the same time enjoys a favorable fairness-utility tradeoff.
A Framework For Differentiable Discovery Of Graph Algorithms
Authors: Hanjun Dai, Xinshi Chen, Yu Li, Xin Gao, Le Song
Date: 2021-01-01 00:00:00
Proceeding: NeurIPS Workshop LMCA 2020 12
Tasks: Tree Decomposition

Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms. However, these works focus more on imitating existing algorithms, and are limited in two important aspects: the search space for algorithms is too small and the learned GNN models are not interpretable. To address these issues, we propose a novel framework which enlarge the search space using cheap global information from tree decomposition of the graphs, and can explain the structures of the graph leading to the decision of learned algorithms. We apply our framework to three NP-complete problems on graphs and show that the framework is able to discover effective and explainable algorithms.
KETG: A Knowledge Enhanced Text Generation Framework
Authors: Yan Cui, Xi Chen, Jiang Qian, Bojin Zhuang, Shaojun Wang, Jing Xiao
Date: 2021-01-01 00:00:00
Tasks: Diversity, Text Generation

Embedding logical knowledge information into text generation is a challenging NLP task. In this paper, we propose a knowledge enhanced text generation (KETG) framework, which incorporates both the knowledge and associated text corpus to address logicality and diversity in text generation. Specifically, we validate our framework on rhetorical text generation from our newly built rhetoric knowledge graph. Experiments show that our framework outperforms baseline models such as Transformer and GPT-2, on rhetorical type control, semantic comprehensibility and diversity.
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space
Authors: Tsz-Him Cheung, Dit-yan Yeung
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Data Augmentation, Time Series, Time Series Analysis

Data augmentation is an efficient way to expand a training dataset by creating additional artificial data. While data augmentation is found to be effective in improving the generalization capability of models for various machine learning tasks, the underlying augmentation methods are usually manually designed and carefully evaluated for each data modality separately, like image processing functions for image data and word-replacing rules for text data. In this work, we propose an automated data augmentation approach called MODALS (Modality-agnostic Automated Data Augmentation in the Latent Space) to augment data for any modality in a generic way. MODALS exploits automated data augmentation to fine-tune four universal data transformation operations in the latent space to adapt the transform to data of different modalities. Through comprehensive experiments, we demonstrate the effectiveness of MODALS on multiple datasets for text, tabular, time-series and image modalities.
BUTLER: Building Understanding in TextWorld via Language for Embodied Reasoning
Authors: Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Cote, Yonatan Bisk, Adam Trischler, Matthew Hausknecht
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Scene Understanding

Given a simple request (e.g., Put a washed apple in the kitchen fridge), humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text-based policies in TextWorld (Côté et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER’s simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, visual scene understanding, and so forth).
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations
Authors: Weihao Gao, Xiangjun Fan, Jiankai Sun, Kai Jia, Wenzhi Xiao, Chong Wang, Xiaobing Liu
Date: 2021-01-01 00:00:00
Tasks: Retrieval

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model and then use maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper, we present Deep Retrieval (DR), an end-to-end learnable structure model for large-scale recommendations. DR encodes all candidates into a discrete latent space. Those latent codes for the candidates are model parameters and to be learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates. Empirically, we showed that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline.
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay
Authors: Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel
Date: 2021-01-01 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations. Experience replay improves sample-efficiency by reusing experiences from the past, and convolutional neural networks (CNNs) process high-dimensional inputs effectively. However, such techniques demand high memory and computational bandwidth. In this paper, we present Latent Vector Experience Replay (LeVER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements without sacrificing the performance of RL agents. To reduce the computational overhead of gradient updates in CNNs, we freeze the lower layers of CNN encoders early in training due to early convergence of their parameters. Additionally, we reduce memory requirements by storing the low-dimensional latent vectors for experience replay instead of high-dimensional images, enabling an adaptive increase in the replay buffer capacity, a useful technique in constrained-memory settings. In our experiments, we show that LeVER does not degrade the performance of RL agents while significantly saving computation and memory across a diverse set of DeepMind Control environments and Atari games. Finally, we show that LeVER is useful for computation-efficient transfer learning in RL because lower layers of CNNs extract generalizable features, which can be used for different tasks and domains.
Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection
Authors: Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
Date: 2021-01-01 00:00:00
Tasks: Contrastive Learning

This work aims to enable efficient on-device contrastive learning from input streaming data after a model is deployed on edge devices such as robots or unmanned aerial vehicles (UAVs) so that they can adapt to a dynamic new environment for higher accuracy. On the other hand, such data usually does not have any labels, calling for unsupervised learning. Most recently, contrastive learning has demonstrated its great potential in learning visual representation from unlabeled data. However, directly applying it to streaming data requires storing a large dataset on-the-fly, which will quickly drain edge devices’ storage resources. In this paper, we propose a framework to automatically select the most representative data from unlabeled input stream on-the-fly, which only requires the use of a small data buffer for dynamic learning. What is more, considering the fact that the data are not independent and identically distributed (iid) as in the traditional training process, we score new data as they come in by measuring the quality of their representations without requiring any label information, based on which the data in the buffer will be updated. Extensive experiments show that the learning speed and accuracy are greatly improved compared with approaches without data selection.
ROGA: Random Over-sampling Based on Genetic Algorithm
Authors: ZONGDA HAN, XIUQUAN QIAO, SHUBO ZHAN
Date: 2021-01-01 00:00:00

When using machine learning to solve practical tasks, we often face the problem of class imbalance. Unbalanced classes will cause the model to generate preferences during the learning process, thereby ignoring classes with fewer samples. The oversampling algorithm achieves the purpose of balancing the difference in quantity by generating a minority of samples. The quality of the artificial samples determines the impact of the oversampling algorithm on model training. Therefore, a challenge of the oversampling algorithm is how to find a suitable sample generation space. However, too strong conditional constraints can make the generated samples as non-noise points as possible, but at the same time they also limit the search space of the generated samples, which is not conducive to the discovery of better-quality new samples. Therefore, based on this problem, we propose an oversampling algorithm ROGA based on genetic algorithm. Based on random sampling, new samples are gradually generated and the samples that may become noise are filtered out. ROGA can ensure that the sample generation space is as wide as possible, and it can also reduce the noise samples generated. By verifying on multiple datasets, ROGA can achieve a good result.
Fast and Differentiable Matrix Inverse and Its Extension to SVD
Authors: Xingyu Xie, Hao Kong, Jianlong Wu, Guangcan Liu, Zhouchen Lin
Date: 2021-01-01 00:00:00

Matrix inverse (Minv) and singular value decomposition (SVD) are among the most widely used matrix operations in massive data analysis, machine learning, and statistics.  Although well-studied, they still encounter difficulties in practical use due to inefficiency and non-differentiability.  In this paper, we aim at solving efficiency and differentiability issues through learning-based methods.  First of all, to perform matrix inverse, we provide a differentiable yet efficient way, named LD-Minv, which is a learnable deep neural network (DNN) with each layer being an $L$-th order matrix polynomial. We show that, with proper initialization, the difference between LD-Minv's output and exact pseudo-inverse is in the order $O(exp{-L^K})$ where $K$ is the depth of the LD-Minv. Moreover, by learning from data, LD-Minv further reduces the difference between the output and the exact pseudo-inverse. We prove that gradient descent finds an $\epsilon$-error minimum within $O(nKL\log(1/\epsilon))$ steps for LD-Minv, where n is the data size.  At last, we provide the generalization bound for LD-Minv in both under-parameterized and over-parameterized settings. As an application of LD-Minv, we provide a learning-based optimization method to solve the problem with orthogonality constraints and utilize it to differentiate SVD (D-SVD). We also offer a theoretical generalization guarantee for D-SVD. Finally, we demonstrate the superiority of our methods on the synthetic and real data in the supplementary materials.
Practical Marginalized Importance Sampling with the Successor Representation
Authors: Scott Fujimoto, David Meger, Doina Precup
Date: 2021-01-01 00:00:00
Tasks: Deep Reinforcement Learning, MuJoCo, Off-policy evaluation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on simple toy problems. We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy. The successor representation can be trained through deep reinforcement learning methodology and decouples the reward optimization from the dynamics of the environment, making the resulting algorithm stable and applicable to high-dimensional domains. We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.
F^2ed-Learning: Good Fences Make Good Neighbors
Authors: Lun Wang, Qi Pang, Shuai Wang, Dawn Song
Date: 2021-01-01 00:00:00
Tasks: Federated Learning

In this paper, we present F^2ed-Learning, the first federated learning protocol simultaneously defending against both semi-honest server and Byzantine malicious clients. Using a robust mean estimator called FilterL2, F^2ed-Learning is the first FL protocol with dimension-free estimation error against Byzantine malicious clients. Besides, F^2ed-Learning leverages secure aggregation to protect the clients from a semi-honest server who wants to infer the clients' information from the legitimate updates. The main challenge stems from the incompatibility between FilterL2 and secure aggregation. Specifically, to run FilterL2, the server needs to access individual updates from clients while secure aggregation hides those updates from it. We propose to split the clients into shards, securely aggregate each shard's updates and run FilterL2 on the updates from different shards. The evaluation shows that F^2ed-Learning consistently achieves optimal or sub-optimal performance under three attacks among five robust FL protocols. The code for evaluation is available in the supplementary material.
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning
Authors: Hengrui Cai, Rui Song, Wenbin Lu
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1

In the era of causal revolution, identifying the causal effect of an exposure on the outcome of interest is an important problem in many areas, such as epidemics, medicine, genetics, and economics. Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators. An analysis of causal effects that interprets the causal mechanism contributed through mediators is hence challenging but on demand. To the best of our knowledge, there are no feasible algorithms that give an exact decomposition of the indirect effect on the level of individual mediators, due to common interaction among mediators in the complex graph. In this paper, we establish a new statistical framework to comprehensively characterize causal effects with multiple mediators, namely, ANalysis Of Causal Effects (ANOCE), with a newly introduced definition of the mediator effect, under the linear structure equation model. We further propose a constrained causal structure learning method by incorporating a novel identification constraint that specifies the temporal causal relationship of variables. The proposed algorithm is applied to investigate the causal effects of 2020 Hubei lockdowns on reducing the spread of the coronavirus in Chinese major cities out of Hubei.
D2p-fed:Differentially Private Federated Learning with Efficient Communication
Authors: Lun Wang, Ruoxi Jia, Dawn Song
Date: 2021-01-01 00:00:00
Tasks: Federated Learning

In this paper, we propose the discrete Gaussian based differentially private federated learning (D2p-fed), a unified scheme to achieve both differential privacy (DP) and communication efficiency in federated learning (FL). In particular, compared with the only prior work taking care of both aspects, D2p-fed provides stronger privacy guarantee, better composability and smaller communication cost. The key idea is to apply the discrete Gaussian noise to the private data transmission. We provide complete analysis of the privacy guarantee, communication cost and convergence rate of D2p-fed. We evaluated D2p-fed on INFIMNIST and CIFAR10. The results show that D2p-fed outperforms the-state-of-the-art by 6.7% to 9.75% in terms of model accuracy while saving one third of the communication cost. The code for evaluation is available in the supplementary material.
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks
Authors: Glen Berseth, Florian Golemo, Christopher Pal
Date: 2021-01-01 00:00:00
Tasks: One-Shot Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

It would be desirable for a reinforcement learning (RL) based agent to learn behaviour by merely watching a demonstration. However, defining rewards that facilitate this goal within the RL paradigm remains a challenge. Here we address this problem with Siamese networks, trained to compute distances between observed behaviours and an agent's behaviours. We use an RNN-based comparator model to learn such distances in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we have also found that the inclusion of multi-task data and an additional image encoding loss helps enforce temporal consistency and improve policy learning. These two components appear to balance reward for matching a specific instance of a behaviour versus that behaviour in general. Furthermore, we focus here on a particularly challenging form of this problem where only a single demonstration is provided for a given task -- the one-shot learning setting. We demonstrate our approach on humanoid, dog and raptor agents in 2D and a 3D quadruped and humanoid. In these environments, we show that our method outperforms the state-of-the-art, GAIfO (i.e. GAIL without access to actions) and TCNs.
NOSE Augment: Fast and Effective Data Augmentation Without Searching
Authors: Qingrui Li, Song Xie, Anıl Oymagil, Mustafa Furkan Eseoglu, Ziyin Zhang, CM Lee
Date: 2021-01-01 00:00:00
Tasks: Data Augmentation, Diversity, Face Recognition, image-classification, Image Classification, Optical Character Recognition, Optical Character Recognition (OCR)

Data augmentation has been widely used for enhancing the diversity of training data and model generalization. Different from traditional handcrafted methods, recent research introduced automated search for optimal data augmentation policies and achieved state-of-the-art results on image classification tasks. However, these search-based implementations typically incur high computation cost and long search time because of large search spaces and complex searching algorithms. We revisited automated augmentation from alternate perspectives, such as increasing diversity and manipulating overall usage of augmented data. In this paper, we present an augmentation method without policy searching called NOSE Augment (NO SEarch Augment). Our method completely skips policy searching and instead adopts a stochastic policy strategy. To further boost the data diversity of stochastic augmentation, NOSE introduces new augmentation operations into the operation pool. We further propose the introduction of a multi-stage complexity driven strategy for ensuring a smooth convergence to a good quality model. We conduct extensive experiments and showed that our method could match or surpass state-of-the-art results provided by search-based methods in terms of accuracies. Without the need for policy search, our method is much more efficient than the existing AutoAugment series of implementations. Besides image classification task, we also examine the general validity of our proposed method by applying our method to Face Recognition and Optical Character Recognition (OCR) tasks. The results establish our proposed method as a fast and competitive data augmentation strategy that can be used across various CV tasks.