GenAI4ELab/papercli-papers huggingface dataset:
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
Authors: Yeonsung Jung; Hajin Shim; June Yong Yang; Eunho Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite their remarkable ability to generalize with over-capacity networks, deep neural networks often abuse bias instead of using the actual task-related information for discriminative tasks. Since such shortcuts are only effective within the collected dataset, the resulting biased model underperforms on real-world inputs. To counteract the influence of bias, existing methods either exploit auxiliary information which is rarely obtainable in practice, or sift bias-free samples to exploit them for debiasing. However, such presumptions about the availability of the auxiliary information or bias-free samples are not always guaranteed and the existing methods could break down due to the unmet presumptions. In this paper, we propose Contrastive Debiasing via Generative Bias-transformation (CDvG) which is capable of operating without exploiting bias labels and bias-free samples explicitly. Motivated by our observation that not only discriminative models but also image translation models tend to focus on the easy-to-learn bias, CDvG employs a image translation model to transform the bias to another mode of bias while preserving task-relevant information. Through contrastive learning, we set transformed biased views against another, learning bias-invariant representations. Especially, as the bias has a stronger correlation or is easier to perceive compared to the signal, the translation model is more likely to be a bias translation model, resulting in better debiasing effect. Experimental results demonstrate that CDvG outperforms the state-of-the-arts, especially when bias-free samples are extremely scarce.
Representation Interference Suppression via Non-linear Value Factorization for Indecomposable Markov Games
Authors: Lipeng Wan; Xu He; Zeyang Liu; Kai Li; Mengchen Zhao; Dong Li; Bo An; Jianye HAO; Xuguang Lan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Value factorization is an efficient approach for centralized training with decentralized execution in cooperative multi-agent reinforcement learning tasks. As the simplest implementation of value factorization, Linear Value Factorization (LVF) attracts wide attention. In this paper, firstly, we investigate the applicable conditions of LVF, which is important but usually neglected by previous works. We prove that due to the representation limitation, LVF is only perfectly applicable to an extremely narrow class of tasks, which we define as the decomposable Markov game. Secondly, to handle the indecomposable Markov game where the LVF is inapplicable, we turn to value factorization with complete representation capability (CRC) and explore the general form of the value factorization function that satisfies both Independent Global Max (IGM) and CRC conditions. A common problem of these value factorization functions is the representation interference among true Q values with shared local Q value functions. As a result, the policy could be trapped in local optimums due to the representation interference on the optimal true Q values. Thirdly, to address the problem, we propose a novel value factorization method, namely Q Factorization with Representation Interference Suppression (QFRIS).  QFRIS adaptively reduces the gradients of the local Q value functions contributed by the non-optimal true Q values. Our method is evaluated on various benchmarks. Experimental results demonstrate the good convergence of QFIRS.
Graph Contrastive Learning with Model Perturbation
Authors: Qiaoyu Tan; Sirui Ding; Ninghao Liu; Soo-Hyun Choi; Li Li; Rui Chen; Xia Hu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graph contrastive learning (GCL) has achieved great success in pre-training graph neural networks (GNN) without ground-truth labels. The performance of GCL mainly rely on designing high quality contrastive views via data augmentation. However, finding desirable augmentations is difficult and requires cumbersome efforts due to the diverse modalities in graph data. In this work, we study model perturbation to perform efficient contrastive learning on graphs without using data augmentation. Instead of searching for the optimal combination among perturbing nodes, edges or attributes, we propose to conduct perturbation on the model architectures (i.e., GNNs). However, it is non-trivial to achieve effective perturbations on GNN models without performance dropping compared with its data augmentation counterparts. This is because data augmentation 1) makes complex perturbation in the graph space, so it is hard to mimic its effect in the model parameter space with a fixed noise distribution, and 2) has different disturbances even on the same nodes between two views owning to the randomness. Motivated by this, we propose a novel model perturbation framework -- \textsc{PerturbGCL} to pre-train GNN encoders. We focus on perturbing two key operations in a GNN, including message propagation and transformation. Specifically, we propose \emph{weightPrune} to create a dynamic perturbed model to contrast with the target one by pruning its transformation weights according to their magnitudes. Contrasting the two models will lead to adaptive mining of the perturbation distribution from the data. Furthermore, we present \emph{randMP} to disturb the steps of message propagation in two contrastive models. By randomly choosing the propagation steps during training, it helps to increase local variances of nodes between the contrastive views.  Despite the simplicity, coupling the two strategies together enable us to perform effective contrastive learning on graphs with model perturbation. We conduct extensive experiments on 15 benchmarks. The results demonstrate the superiority of \textsc{PerturbGCL}: it can achieve competitive results against strong baselines across both node-level and graph-level tasks, while requiring shorter computation time. The code is available at \url{https://anonymous.4open.science/r/PerturbGCL-F17D}.
Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning
Authors: Bo Wan; Yongfei Liu; Desen Zhou; Tinne Tuytelaars; Xuming He
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challenging due to ambiguous human-object associations, large search space of detecting HOIs and highly noisy training signal. A promising strategy to address those challenges is to exploit knowledge from large-scale pretrained models (e.g., CLIP), but a direct knowledge distillation strategy does not perform well on the weakly-supervised setting. In contrast, we develop a CLIP-guided HOI representation capable of incorporating the prior knowledge at both image level and HOI instance level, and adopt a self-taught mechanism to prune incorrect human-object associations. Experimental results on HICO-DET and V-COCO
show that our method outperforms the previous works by a sizable margin, showing the efficacy of our HOI representation.
Multivariate Time-series Imputation with Disentangled Temporal Representations
Authors: SHUAI LIU; Xiucheng Li; Gao Cong; Yile Chen; YUE JIANG
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Multivariate time series often faces the problem of missing value. Many time series imputation methods have been developed in the literature. However, these methods all rely on an entangled representation to model dynamics of time series, which may fail to fully exploit the multiple factors (e.g., periodic patterns) contained in the time series. Moreover, the entangled representation usually has no semantic meaning, and thus they often lack interpretability. In addition, many recent models are proposed to deal with the whole time series to capture cross-channel correlations and identify temporal dynamics, but they are not scalable to large-scale datasets. Different from existing approaches, we propose TIDER, a novel matrix factorization-based method with disentangled temporal representations that account for multiple factors, namely trend, seasonality, and local bias, to model complex dynamics. The learned disentanglement makes the imputation process more reliable and offers explainability for imputation results. Moreover, TIDER is scalable to large datasets. Empirical results show that our method not only outperforms existing approaches by notable margins on three real-world datasets, but also scales well to large datasets on which existing deep learning based methods struggle. Disentanglement validation experiments further demonstrate the robustness of our model in obtaining accurate and explainable disentangled components.
A Learning Based Hypothesis Test for Harmful Covariate Shift
Authors: Tom Ginsberg; Zhongyuan Liang; Rahul G Krishnan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The ability to quickly and accurately identify covariate shift at test time is a critical and often overlooked component of safe machine learning systems deployed in high-risk domains. While methods exist for detecting when predictions should not be made on out-of-distribution test examples, identifying distributional level differences between training and test time can help determine when a model should be removed from the deployment setting and retrained. In this work, we define harmful covariate shift (HCS) as a change in distribution that may weaken the generalization of a predictive model. To detect HCS, we use the discordance between an ensemble of classifiers trained to agree on training data and disagree on test data. We derive a loss function for training this ensemble and show that the disagreement rate and entropy represent powerful discriminative statistics for HCS. Empirically, we demonstrate the ability of our method to detect harmful covariate shift with statistical certainty on a variety of high-dimensional datasets. Across numerous domains and modalities, we show state-of-the-art performance compared to existing methods, particularly when the number of observed test samples is small.
Compressed Predictive Information Coding
Authors: Rui Meng; Tianyi Luo; Kristofer Bouchard
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Unsupervised learning plays an important role in many fields, such as machine learning, data compression, and neuroscience. Compared to static data, methods for extracting low-dimensional structure for dynamic data are lagging. We developed a novel information-theoretic framework, Compressed Predictive Information Coding (CPIC), to extract predictive latent representations from dynamic data. Predictive information quantifies the ability to predict the future of a time series from its past. CPIC selectively projects the past (input) into a low dimensional space that is predictive about the compressed data projected from the future (output). The key insight of our framework is to learn representations by balancing the minimization of compression complexity with maximization of the predictive information in the latent space. We derive tractable variational bounds of the CPIC loss by leveraging bounds on mutual information. The CPIC loss induces the latent space to capture information that is maximally predictive of the future of the data from the past. We demonstrate that introducing stochasticity in the encoder and maximizing the predictive information in latent space contributes to learning more robust latent representations. Furthermore, our variational approaches perform better in mutual information estimation compared with estimates under the Gaussian assumption commonly used. We show numerically in synthetic data that CPIC can recover dynamical systems embedded in noisy observation data with low signal-to-noise ratio. Finally, we demonstrate that CPIC extracts features more predictive of forecasting exogenous variables as well as auto-forecasting in various real datasets compared with other state-of-the-art representation learning models. Together, these results indicate that CPIC will be broadly useful for extracting low-dimensional dynamic structure from high-dimensional, noisy time-series data.
Improving Deep Regression with Ordinal Entropy
Authors: Shihao Zhang; Linlin Yang; Michael Bi Mi; Xiaoxu Zheng; Angela Yao
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In computer vision, it is often observed that formulating regression problems as a classification task yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression.
On the convergence of SGD under the over-parameter setting
Authors: ruinan Jin; Yiwei Wang; Wei Liu; Baoxiang Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: With the improvement of computing power, over-parameterized models get increasingly popular in machine learning. This type of model is usually with a complicated, non-smooth, and non-convex loss function landscape. However, when we train the model, simply using the first-order optimization algorithm like stochastic gradient descent (SGD) could acquire some good results, in both training and testing, albeit that SGD is known to not guarantee convergence for non-smooth and non-convex cases. Theoretically, it was previously proved that in training, SGD converges to the global optimum with probability $1 - \epsilon$, but only for certain models and $\epsilon$ depends on the model complexity. It was also observed that SGD tends to choose a flat minimum, which preserves its training performance in testing. In this paper, we first prove that SGD could iterate to the global optimum almost surely under arbitrary initial value and some mild assumptions on the loss function. Then, we prove that if the learning rate is larger than a value depending on the structure of a global minimum, the probability of converging to this global optimum is zero. Finally, we acquire the asymptotic convergence rate based on the local structure of the global optimum.
DEEPER-GXX: DEEPENING ARBITRARY GNNS
Authors: Lecheng Zheng; Dongqi Fu; Ross Maciejewski; Jingrui He
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recently, motivated by real applications, a major research direction in graph neural networks (GNNs) is to explore deeper structures.
For instance, the graph connectivity is not always consistent with the label distribution (e.g., the closest neighbors of some nodes are not from the same category). In this case, GNNs need to stack more layers, in order to find the same categorical neighbors in a longer path for capturing the class-discriminative information. However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i.e., vanishing gradient and over-smoothing. On one hand, stacking layers makes the neural network hard to train as the gradients of the first few layers vanish. Moreover, when simply addressing vanishing gradient in GNNs, we discover the shading neighbors effect (i.e., stacking layers inappropriately distorts the non-IID information of graphs and degrade the performance of GNNs). On the other hand, deeper GNNs aggregate much more information from common neighbors such that individual node representations share more overlapping features, which makes the final output representations not discriminative (i.e., overly smoothed). In this paper, for the first time, we address both problems to enable deeper GNNs, and propose Deeper-GXX, which consists of the Weight-Decaying Graph Residual Connection module (WDG-ResNet) and Topology-Guided Graph Contrastive Loss (TGCL). Extensive experiments on real-world data sets demonstrate that Deeper-GXX outperforms state-of-the-art deeper baselines.
Graph Neural Networks as Multi-View Learning
Authors: Haoyu Han; Xiaorui Liu; Haitao Mao; MohamadAli Torkamani; Feng Shi; Victor Lee; Jiliang Tang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graph Neural Networks (GNNs) have demonstrated powerful representation capability in semi-supervised node classification. In this task, there are often three types of information  -- graph structure, node features, and node labels. Existing GNNs usually leverage both node features and graph structure by feature transformation and aggregation, following end-to-end training via node labels. In this paper, we change our perspective by considering these three types of information as three views of nodes. This perspective motivates us to design a new GNN framework as multi-view learning which enables alternating optimization training instead of end-to-end training,  resulting in significantly improved computation and memory efficiency. Extensive experiments with different settings demonstrate the effectiveness and efficiency of the proposed method.
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Authors: Renhao Wang; Jiayuan Mao; Joy Hsu; Hang Zhao; Jiajun Wu; Yang Gao
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent works have integrated semantic representations from large-scale pretrained vision-language (VL) models into manipulation models, imparting them with more general reasoning capabilities. However, we show that the conventional {\it pretraining-finetuning} pipeline for integrating such representations entangles the learning of domain-specific action information and domain-general visual information, leading to less data-efficient training and poor generalization to unseen objects and tasks. To this end, we propose \ours, a {\it modular} approach to better leverage pretrained VL models by exploiting the syntactic and semantic structures of language instructions. Our framework uses a semantic parser to recover an executable program, composed of functional modules grounded on vision and action across different modalities. Each functional module is realized as a combination of deterministic computation and learnable neural networks. Program execution produces parameters to general manipulation primitives for a robotic end-effector. The entire modular network can be trained with end-to-end imitation learning objectives. Experiments show that our model successfully disentangles action and perception, translating to improved zero-shot and compositional generalization in a variety of manipulation behaviors. Project webpage at: \url{https://progport.github.io}.
Adversarial Cheap Talk
Authors: Chris Lu; Timon Willi; Alistair Letcher; Jakob Nicolaus Foerster
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adversarial attacks in reinforcement learning (RL) often assume highly-privileged access to the victim’s parameters, environment, or data. Instead, this paper proposes a novel adversarial setting called a Cheap Talk MDP in which an Adversary can merely append deterministic messages to the Victim’s observation, resulting in a minimal range of influence. The Adversary cannot occlude ground truth, influence underlying environment dynamics or reward signals, introduce non-stationarity, add stochasticity, see the Victim’s actions, or access their parameters. Additionally, we present a simple meta-learning algorithm called Adversarial Cheap Talk (ACT) to train Adversaries in this setting. We demonstrate that an Adversary trained with ACT can still significantly influence the Victim’s training and testing performance, despite the highly constrained setting. Affecting train-time performance reveals a new attack vector and provides insight into the success and failure modes of existing RL algorithms. More specifically, we show that an ACT Adversary is capable of harming performance by interfering with the learner’s function approximation, or instead helping the Victim’s performance by outputting useful features. Finally, we show that an ACT Adversary can manipulate messages during train-time to directly and arbitrarily control the Victim at test-time.
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier
Authors: Renzhen Wang; Xixi Jia; Quanziang Wang; Yichen Wu; Deyu Meng
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data are balanced. However, such an assumption is far from realistic scenarios and thus severely limits the performance of current pseudo-labeling methods under the context of class-imbalance. To alleviate this problem, we design a bias adaptive classifier that targets the imbalanced SSL setups. The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier. The bias attractor is designed as a light-weight residual network and learned through a bi-level learning framework, which enables the bias adaptive classifier to fit imbalanced training data, while the linear classifier can provide unbiased label prediction for each class. We conduct extensive experiments under various imbalanced semi-supervised setups, and the results demonstrate that our method can be applied to different pseudo-labeling models and is superior to current state-of-the-art methods.
Test-time Adaptation for Better Adversarial Robustness
Authors: Zhichao Huang; Chen Liu; Mathieu Salzmann; Sabine Süsstrunk; Tong Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Standard adversarial training and its variants have been widely adopted in practice to achieve robustness against adversarial attacks. However, we show in this work that such an approach does not necessarily achieve near optimal generalization performance on test samples. Specifically it is shown that under suitable assumptions, Bayesian optimal robust estimator requires test-time adaptation, and such adaptation can lead to significant performance boost over standard adversarial training. Motivated by this observation, we propose a practically easy to implement method to improve the generalization performance of adversarially-trained networks via an additional self-supervised test-time adaptation step. We further employs a meta adversarial training method to find a good starting point for test-time adaptation, which incorporates the test-time adaptation procedure into the training phase and it strengthens the correlation between the pre-text tasks in self-supervised learning and the original classification task. Extensive empirical experiments on CIFAR10, STL10 and Tiny ImageNet using several different self-supervised tasks show that our method consistently improves the robust accuracy of standard adversarial training under different white-box and black-box attack strategies.