pwc-archive/papers-with-abstracts huggingface dataset:
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data
Authors: Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee Kwoh, XiaoLi Li
Date: 2021-09-29 00:00:00
Tasks: Benchmarking, Domain Adaptation, Model Selection, Time Series, Time Series Analysis, Unsupervised Domain Adaptation

Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application to time series data is less explored. Existing works on time series domain adaptation suffer from inconsistencies in evaluation schemes, datasets, and base neural network architectures. Moreover, labeled target data are usually employed for model selection, which violates the fundamental assumption of unsupervised domain adaptation. To address these issues, we propose AdaTime, a standard framework to systematically and fairly evaluate different domain adaptation methods on time series data. Specifically, we standardize the base neural network architectures and benchmarking datasets, while also exploring more realistic model selection approaches that can work with no labeled data or few labeled samples. Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data. We conduct extensive experiments to evaluate 10 state-of-the-art methods on 3 representative datasets spanning 15 cross-domain scenarios. Our results suggest that with careful selection of hyper-parameters, visual domain adaptation methods are competitive with methods proposed for time series domain adaptation. In addition, we find that model selection plays a key role and different selection strategies can significantly affect performance. Our work unveils practical insights for applying domain adaptation methods on time series data and builds a solid foundation for future works in the field.
Deep Encryption: Protecting Pre-Trained Neural Networks with Confusion Neurons
Authors: Mengbiao Zhao, Shixiong Xu, Jianlong Chang, Lingxi Xie, Jie Chen, Qi Tian
Date: 2021-09-29 00:00:00
Tasks: Position

Having consumed huge amounts of training data and computational resource, large-scale pre-trained models are often considered key assets of AI service providers. This raises an important problem: how to prevent these models from being maliciously copied when they are running on customers' computing device? We answer this question by adding a set of confusion neurons into the pre-trained model, where the position of these neurons is encoded into a few integers that are easy to be encrypted. We find that most often, a small portion of confusion neurons are able to effectively contaminate the pre-trained model. Thereafter, we extend our study to a bigger picture that the customers may develop algorithms to eliminate the effect of confusion neurons and recover the original network, and we show that our simple approach is somewhat capable of defending itself against the fine-tuning attack.
DM-CT: Consistency Training with Data and Model Perturbation
Authors: Xiaobo Liang, Runze Mao, Lijun Wu, Juntao Li, Weiqing Liu, Qing Li, Min Zhang
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, Machine Translation, Translation

Consistency training has been widely adopted and shown great promise in deep learning. The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability. Recently, consistency training is also incorporated from the model-level, in which the  randomness existed in the model (e.g., dropout) is constrained during the training stage, and the inference model can be more consistent with the training phase. In this work, we investigate these two aspects and propose an integrated framework, DM-CT, that incorporates both the data-level and model-level consistency training. Concretely, the input data is first augmented, and the output distributions of different sub models generated by model variance are forced to be consistent (model-level). Meanwhile, the predictions of the original input and the augmented one are constrained to be consistent (data-level). We study different data augmentation strategies and model variances in the DM-CT framework. Experiments on different tasks, including neural machine translation ($4$ IWSLT14 translation tasks, multilingual translation task, and WMT16 Romanian$\to$English translation), natural language understand (GLUE benchmark), and image classification (CIFAR-100 dataset), well demonstrate the superiority of DM-CT by obtaining significant and consistent performance improvements.
Attention: Self-Expression Is All You Need
Authors: Rene Vidal
Date: 2021-09-29 00:00:00
Tasks: All, Clustering

Transformer models have achieved significant improvements in performance for various learning tasks in natural language processing and computer vision. Much of their success is attributed to the use of attention layers that capture long-range interactions among data tokens (such as words and image patches) via attention coefficients that are global and adapted to the input data at test time. In this paper we study the principles behind attention and its connections with prior art. Specifically, we show that attention builds upon a long history of prior work on manifold learning and image processing, including methods such as kernel-based regression, non-local means, locally linear embedding, subspace clustering and sparse coding. Notably, we show that self-attention is closely related to the notion of self-expressiveness in subspace clustering, wherein data points to be clustered are expressed as linear combinations of other points with global coefficients that are adapted to the data and capture long-range interactions among data points. We also show that heuristics in sparse self-attention can be studied in a more principled manner using prior literature on sparse coding and sparse subspace clustering. We thus conclude that the key innovations of attention mechanisms relative to prior art are the use of many learnable parameters, and multiple heads and layers.
Expected Improvement-based Contextual Bandits
Authors: Hung Tran-The, Sunil Gupta, Santu Rana, Long Tran-Thanh, Svetha Venkatesh
Date: 2021-09-29 00:00:00
Tasks: Bayesian Optimization, Multi-Armed Bandits, Thompson Sampling

The expected improvement (EI) is a popular technique to handle the tradeoff between exploration and exploitation under uncertainty. However, compared to other techniques as Upper Confidence Bound (UCB) and Thompson Sampling (TS), the theoretical properties of EI have not been well studied even for non-contextual settings such as standard bandit and Bayesian optimization. In this paper, we introduce and study the EI technique as a new tool for the contextual bandit problem which is a generalization of the standard bandit. We propose two novel EI-based algorithms for this problem, one when the reward function is assumed to be linear and the other when no assumption is made about the reward function other than it being bounded. With a linear reward function, we demonstrate that our algorithm achieves a near-optimal regret. In particular, our regret bound reduces a factor of $\sqrt{\text{log}(T)}$ compared to the popular OFUL algorithm \citep{Abbasi11} which uses the UCB approach, and reduces a factor of $\sqrt{d\text{log}(T)}$ compared to another popular algorithm \citep{agrawal13} which uses the TS approach. Here $T$ is the horizon and $d$ is the feature vector dimension. Further, when no assumptions are made about the form of reward, we use deep neural networks to model the reward function. We prove that this algorithm also achieves a near-optimal regret. Finally, we provide an empirical evaluation of the algorithms on both synthetic functions and various benchmark datasets. Our experiments show that our algorithms work well and consistently outperform existing approaches.
Equalized Robustness: Towards Sustainable Fairness Under Distributional Shifts
Authors: Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang
Date: 2021-09-29 00:00:00
Tasks: Fairness

Increasing concerns have been raised on deep learning fairness in recent years. Existing fairness metrics and algorithms mainly focus on the discrimination of model performance across different groups on in-distribution data. It remains unclear whether the fairness achieved on in-distribution data can be generalized to data with unseen distribution shifts, which are commonly encountered in real-world applications. In this paper, we first propose a new fairness goal, termed Equalized Robustness (ER), to impose fair model robustness against unseen distribution shifts across majority and minority groups. ER measures robustness disparity by the maximum mean discrepancy (MMD) distance between the loss curvature distributions of two groups of data. We show that previous fairness learning algorithms designed for in-distribution fairness fail to meet the new robust fairness goal. We further propose a novel fairness learning algorithm, termed Curvature Matching (CUMA), to simultaneously achieve both traditional in-distribution fairness and our new robust fairness. CUMA efficiently debiases the model robustness by minimizing the MMD distance between loss curvature distributions of two groups. Experiments on three popular datasets show CUMA achieves superior fairness in robustness against distribution shifts, without more sacrifice on either overall accuracies or the in-distribution fairness.
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting
Authors: Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Causal Inference

We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions pose serious challenges for causal inference. To address this issue, we propose a method that transforms conditional moment restrictions to unconditional moment restrictions through importance weighting using a conditional density ratio estimator. Then, using this transformation, we propose a method that successfully estimate a parametric or nonparametric functions defined under the conditional moment restrictions. We analyze the estimation error and provide a bound on the structural function, providing theoretical support for our proposed method. In experiments, we confirm the soundness of our proposed method.
Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning
Authors: Justin Lazarow, Kihyuk Sohn, Chun-Liang Li, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
Date: 2021-09-29 00:00:00
Tasks: Pseudo Label

While remarkable progress in imbalanced supervised learning has been made recently, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only is a few labeled data provided, but the underlying data distribution can be severely imbalanced. Recent works require both complicated sampling-based strategies of pseudo-labeled data and distribution alignment of the pseudo-label distribution to accommodate this imbalance. We present a novel approach that relies only on a form of a distribution alignment but no sampling strategy where rather than aligning the pseudo-labels during inference, we move the distribution alignment component into the respective cross entropy loss computations for both the supervised and unsupervised losses. This alignment compensates for both imbalance in the data as well as the eventual distributional shift present during evaluation. Altogether, this provides a single, unified strategy that offers both significantly reduced training requirements and improved performance across both low and richly labeled regimes and over varying degrees of imbalance. In experiments, we validate the efficacy of our method on SSL variants of CIFAR10-LT, CIFAR100-LT, and ImageNet-127. On ImageNet-127, our method shows 1.6% accuracy improvement over the previous best method with 80% training time reduction.
Generating Unobserved Alternatives with Tower Implicit Model (TIM)
Authors: Shichong Peng, Seyed Alireza Moazenipourasil, Ke Li
Date: 2021-09-29 00:00:00
Tasks: model, regression

We consider problems where multiple predictions can be considered correct, but only one of them is given as supervision. This setting differs from both the regression and class-conditional generative modelling settings: in the former, there is a unique observed output for each input, which is provided as supervision; in the latter, there are many observed outputs for each input, and many are provided as supervision. Applying either regression methods and conditional generative models to the present setting often results in a model that can only make a single prediction for each input. We explore several problems that have this property and develop an approach, TIM, that can generate multiple high quality predictions given the same input and achieves a reduction of the Fréchet Inception Distance (FID) by 19.6% on average  compared to the baseline.
LASSO: Latent Sub-spaces Orientation for Domain Generalization
Authors: Long Tung Vuong, Trung Quoc Phung, Toan Tran, Anh Tuan Tran, Dinh Phung, Trung Le
Date: 2021-09-29 00:00:00
Tasks: Domain Generalization

To achieve a satisfactory generalization performance on prediction tasks in an unseen domain, existing domain generalization (DG) approaches often rely on the strict assumption of fixed domain-invariant features and common hypotheses learned from a set of training domains. While it is a natural and important premise to ground generalization capacity on the target domain, we argue that this assumption could be overly strict and sub-optimal. It is particularly evident when source domains share little information or the target domains leverages information from selective source domains in a compositional way instead of relying on a unique invariant hypothesis across all source domains. Unlike most existing approaches, instead of constructing a single hypothesis shared among domains, we propose a LAtent Sub-Space Orientation (LASSO) method that explores diverse latent sub-spaces and learning individual hypotheses on those sub-spaces. Moreover, in LASSO, since the latent sub-spaces are formed by the label-informative features captured in source domains, they allow us to project target examples onto appropriate sub-spaces, while preserving crucial label-informative features for the label prediction. Finally, we empirically evaluate our method on several well-known DG benchmarks, where it achieves state-of-the-art results.
Learning Neural Implicit Functions as Object Representations for Robotic Manipulation
Authors: Jung-Su Ha, Danny Driess, Marc Toussaint
Date: 2021-09-29 00:00:00
Tasks: NeRF, Open-Ended Question Answering, Robot Manipulation

Robotic manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasp, placement, tool-use, etc. To achieve such interactions, traditional approaches require hand-designed features and object representations, and it still remains an open question how to describe such interactions with arbitrary objects in a flexible and efficient way. Inspired by neural implicit representations in 3D modeling, e.g. NeRF, we propose a method to represent objects as neural implicit functions upon which we can define and jointly train interaction features. The proposed pixel-aligned representation is directly inferred from camera images with known camera geometry, naturally acting as a perception component in the whole manipulation pipeline, while at the same time enabling sequential robot manipulation planning.
Sharpness-Aware Minimization in Large-Batch Training: Training Vision Transformer In Minutes
Authors: Yong liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You
Date: 2021-09-29 00:00:00

Large-batch training is an important direction for distributed machine learning, which can improve the utilization of large-scale clusters and therefore accelerate the training process. However, recent work illustrates that large-batch training is prone to converge to sharp minima and cause a huge generalization gap. Sharpness-Aware Minimization (SAM) tries to narrow the generalization gap by seeking parameters that lie in a flat region. However, it requires two sequential gradient calculations that doubles the computational overhead. In this paper, we propose a novel algorithm LookSAM to significantly reduce its additional training cost. We further propose a layer-wise modification for adapting LookSAM to the large-batch training setting (Look-LayerSAM). Equipped with our enhanced training algorithm, we are the first to successfully scale up the batch size when training Vision Transformers (ViTs). With a 64k batch size, we are able to train ViTs from scratch within an hour while maintaining competitive performance.
Generative Modeling for Multitask Visual Learning
Authors: Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
Date: 2021-09-29 00:00:00
Tasks: Multi-Task Learning

Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model that is useful across various visual perception tasks. Correspondingly, we propose a general multi-task oriented generative modeling (MGM) framework, by coupling a discriminative multi-task network with a generative network. While it is challenging to synthesize both RGB images and pixel-level annotations in multi-task scenarios, our framework enables us to use synthesized images paired with only weak annotations (i.e., image-level scene labels) to facilitate multiple visual tasks. Experimental evaluation on challenging multi-task benchmarks, including NYUv2 and Taskonomy, demonstrates that our MGM framework improves the performance of all the tasks by large margins, especially in the low-data regimes, and our model consistently outperforms state-of-the-art multi-task approaches.
Bi-linear Value Networks for Multi-goal Reinforcement Learning
Authors: Ge Yang, Zhang-Wei Hong, Pulkit Agrawal
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Multi-Goal Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Universal value functions are used to score the long-term utility of actions to achieve a goal from the current state. In contrast to prior methods that learn a monolithic function to approximate the value, we propose a bi-linear decomposition of the value function. The first component, akin to a global plan models how the state should be changed to reach the goal. The second component, akin to a local controller selects the optimal action to actualize the desired change in state. We simultaneously learn both components. Such decomposition enables both the global and local components to make efficient use of interaction data and independently generalize. The consequence is superior overall generalization and performance of our system on a wide range of challenging goal-conditioned tasks in comparison to the current state-of-the-art.
Pareto Policy Pool for Model-based Offline Reinforcement Learning
Authors: Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: D4RL, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online reinforcement learning (RL) can suffer from poor exploration, sparse reward, insufficient data, and overhead caused by inefficient interactions between an immature policy and a complicated environment. Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model. However, the uncertainty and accuracy of the environment model can drastically vary across different state-action pairs so the RL agent may achieve high model return but perform poorly in the true environment. Unlike previous works that need to carefully tune the trade-off between the model return and uncertainty in a single objective, we study a bi-objective formulation for model-based offline RL that aims at producing a pool of diverse policies on the Pareto front performing different levels of trade-offs, which provides the flexibility to select the best policy for each realistic environment from the pool. Our method, ``Pareto policy pool (P3)'', does not need to tune the trade-off weight but can produce policies allocated at different regions of the Pareto front. For this purpose, we develop an efficient algorithm that solves multiple bi-objective optimization problems with distinct constraints defined by reference vectors targeting diverse regions of the Pareto front. We theoretically prove that our algorithm can converge to the targeted regions. In order to obtain more Pareto optimal policies without linearly increasing the cost, we leverage the achieved policies as initialization to find more Pareto optimal policies in their neighborhoods. On the D4RL benchmark for offline RL, P3 substantially outperforms several recent baseline methods over multiple tasks, especially when the quality of pre-collected experiences is low.
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES
Authors: Wang Tian Xiang, Meiyue Shao, Yanwei Fu, Riheng Jia, Feilong Lin, ZhongLong Zheng
Date: 2021-09-29 00:00:00
Tasks: Federated Learning

This paper studies the problem of information security in the distributed learning framework. In particular, we consider the clients will always be attacked by Byzantine nodes and poisoning in the federated learning. Typically, aggregation rules are utilized to protect the model from the attacks in federated learning. The classical aggregation methods are Krum(·) and Mean(·), which however, are not capable enough to deal with Byzantine attacks in which general deviations and multiple clients are attacked at the same time. We propose new aggregation rules, Tmean(·), to the federated learning algorithm, and propose a federated learning framework based on Byzantine-resilient aggregation algorithm. Our novel Tmean(·) rules are derived from Mean(·) by appropriately trimming some of the values before averaging them. Theoretically, we provide rigorous theoretical proof and understanding of Tmean(·). Extensive experiments validate the effectiveness of our approaches.