GenAI4ELab/papercli-papers huggingface dataset:
Is Self-Supervised Contrastive Learning More Robust Than Supervised Learning?
Authors: Yuanyi Zhong; Haoran Tang; Junkun Chen; Jian Peng; Yu-Xiong Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Prior work on self-supervised contrastive learning has primarily focused on evaluating the recognition accuracy, but has overlooked other behavioral aspects. In addition to accuracy, distributional robustness plays a critical role in the reliability of machine learning models. We design and conduct a series of robustness tests to quantify the behavioral differences between contrastive learning and supervised learning to downstream and pre-training data distribution changes. These tests leverage data corruptions at multiple levels, ranging from pixel-level distortion to patch-level shuffling and to dataset-level distribution shift, including both natural and unnatural corruptions. Our tests unveil intriguing robustness behaviors of contrastive and supervised learning: while we generally observe that contrastive learning is more robust than supervised learning under downstream corruptions, we surprisingly discover the robustness vulnerability of contrastive learning under pixel and patch level corruptions during pre-training. Furthermore, we observe the higher dependence of contrastive learning on spatial image coherence information during pre-training, e.g., it is particularly sensitive to global patch shuffling. We explain these results by connecting to feature space uniformity and data augmentation. Our analysis has implications in improving the downstream robustness of supervised learning, and calls for more studies on understanding contrastive learning.
Batch Normalization and Bounded Activation Functions
Authors: Dongjin Kim; Woojeong Kim; TaeJoo Park; Suhyun Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Since Batch Normalization was proposed, it has been commonly located in front of activation functions, as proposed by the original paper. Swapping the order, i.e., using Batch Normalization after activation functions, has also been attempted, but it is generally not much different from the conventional order when ReLU is used. However, in the case of bounded activation functions like Tanh, we discovered that the swapped order achieves considerably better performance on various benchmarks and architectures than the conventional order. We report this remarkable phenomenon and closely examine what contributes to this performance improvement in this paper. One noteworthy thing about swapped models is the extreme saturation of activation values, which is usually considered harmful. Looking at the output distribution of individual activation functions, we found that many of them are highly asymmetrically saturated. The experiments inducing a different degree of asymmetric saturation support the hypothesis that asymmetric saturation helps improve performance. In addition, we found that Batch Normalization after bounded activation functions has another important effect: it relocates the asymmetrically saturated output of activation functions near zero. This enables the swapped model to have higher sparsity, further improving performance. Extensive experiments with Tanh, LeLecun Tanh, and Softsign show that the swapped models achieve improved performance with a high degree of asymmetric saturation.
Temporary feature collapse phenomenon in early learning of MLPs
Authors: Dongrui Liu; Shaobo Wang; Jie Ren; Kangrui Wang; Sheng Yin; Huiqi Deng; Quanshi Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs). We discover and explain the reason for the feature collapse phenomenon in the first phase, i.e., the diversity of features over different samples keeps decreasing in the first phase, until samples of different categories share almost the same feature, which hurts the optimization of MLPs. We explain such a phenomenon in terms of the learning dynamics of MLPs. Furthermore, we theoretically analyze the reason why four typical operations can alleviate the feature collapse. The code has been attached with the submission.
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
Authors: Xuheng Cai; Chao Huang; Lianghao Xia; Xubin Ren
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing with highly sparse data. Despite their success, most existing graph contrastive learning methods either perform stochastic augmentation (e.g., node/edge perturbation) on the user-item interaction graph, or rely on the heuristic-based augmentation techniques (e.g., user clustering) for generating contrastive views. We argue that these methods cannot well preserve the intrinsic semantic structures and are easily biased by the noise perturbation. In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders. Our model exclusively utilizes singular value decomposition for contrastive augmentation, which enables the unconstrained structural refinement with global collaborative relation modeling. Experiments conducted on several benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the superiority of LightGCL's robustness against data sparsity and popularity bias. The source code of our model is available at https://github.com/HKUDS/LightGCL.
Do We Always Need to Penalize Variance of Losses for Learning with Label Noise?
Authors: Yexiong Lin; Yu Yao; Yuxuan Du; Jun Yu; Bo Han; Mingming Gong; Tongliang Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of losses will improve the stability and generalization of the algorithms. Interestingly, we found that the variance of losses sometimes needs to be increased for the problem of learning with noisy labels. Specifically, increasing the variance of losses would boost the memorization effect and reduce the harmfulness of incorrect labels. Regularizers can be easily designed to increase the variance of losses and be plugged in many existing algorithms. Empirically, the proposed method by increasing the variance of losses could improve the generalization ability of baselines on both synthetic and real-world datasets.
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation
Authors: Jun-Kun Wang; Andre Wibisono
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We consider a setting that a model needs to adapt to a new domain under distribution shifts, given that only unlabeled test samples from the new domain are accessible at test time. A common idea in most of the related works is constructing pseudo-labels for the unlabeled test samples and applying gradient descent (GD) to a loss function with the pseudo-labels. Recently, Goyal et al. (2022) propose conjugate labels, which is a new kind of pseudo-labels for self-training at test time. They empirically show that the conjugate label outperforms other ways of pseudo-labeling on many domain adaptation benchmarks. However, provably showing that GD with conjugate labels learns a good classifier for test-time adaptation remains open. In this work, we aim at theoretically understanding GD with hard and conjugate labels for a binary classification problem. We show that for square loss, GD with conjugate labels converges to an $\epsilon$-optimal predictor under a Gaussian model for any arbitrarily small $\epsilon$, while GD with hard pseudo-labels fails in this task. We also analyze them under different loss functions for the update. Our results shed lights on understanding when and why GD with hard labels or conjugate labels works in test-time adaptation.
Instance-wise Batch Label Restoration via Gradients in Federated Learning
Authors: Kailang Ma; Yu Sun; Jian Cui; Dawei Li; Zhenyu Guan; Jianwei Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Gradient inversion attacks have posed a serious threat to the privacy of federated learning. The attacks search for the optimal pair of input and label best matching the shared gradients and the search space of the attacks can be reduced by pre-restoring labels. Recently, label restoration technique allows for the extraction of labels from gradients analytically, but even the state-of-the-art remains limited to identify the presence of categories (i.e., the class-wise label restoration). This work considers the more real-world settings, where there are multiple instances of each class in a training batch. An analytic method is proposed to perform instance-wise batch label restoration from only the gradient of the final layer. On the basis of the approximate recovered class-wise embeddings and post-softmax probabilities, we establish linear equations of the gradients, probabilities and labels to derive the Number of Instances (NoI) per class by the Moore-Penrose pseudoinverse algorithm. Our experimental evaluations reach over 99% Label existence Accuracy (LeAcc) and exceed 96% Label number Accuracy (LnAcc) in most cases on three image datasets and four classification models. The two metrics are used to evaluate class-wise and instance-wise label restoration accuracy, respectively. And the recovery is made feasible even with a batch size of 4096 and partially negative activations (e.g., Leaky ReLU and Swish). Furthermore, we demonstrate that our method facilitates the existing gradient inversion attacks by exploiting the recovered labels, with an increase of 6-7 in PSNR on both MNIST and CIFAR100. Our code is
available at https://github.com/BUAA-CST/iLRG.
Do Not Blindly Imitate the Teacher: Loss Perturbation for Knowledge Distillation
Authors: Rongzhi Zhang; Jiaming Shen; Tianqi Liu; Jialu Liu; Michael Bendersky; Marc Najork; Chao Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Knowledge distillation (KD) is a popular model compression technique to transfer knowledge from large teacher models to a small student model. Typically, the student learns to imitate the teacher by minimizing the KL divergence of its output distribution with the teacher's output distribution. We argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution, and forcing the student to blindly imitate the unreliable teacher output distribution leads to inferior performance. To this end, we propose a novel knowledge distillation objective PTLoss by first representing the vanilla KL-based distillation loss function via a Maclaurin series and then perturbing the leading-order terms in this series. This perturbed loss improves the student generalizability by effectively distilling knowledge from a shifted distribution closer to the ground truth data. We also propose a method to compute this shifted teacher distribution, named Proxy Teacher, which enables us to select the perturbation coefficients in PTLoss. We theoretically show the perturbed loss reduces the deviation from the true population risk compared to the vanilla KL-based distillation loss functions. Experiments on three tasks with teachers of different scales show that our method significantly outperforms vanilla distillation loss functions and other perturbation methods.
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Authors: Yiming Cui; Linjie Yang; Haichao Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location and category of one specific object from each query. We empirically find that random convex combinations of the learned queries are still good queries for the corresponding models. We then propose to learn a convex combination with dynamic coefficients based on the high-level semantics of the image. The generated dynamic queries better capture the prior of object locations and categories in the different images. Equipped with our dynamic queries, a wide range of DETR-based models achieve consistent and superior performance across multiple tasks (object detection, instance segmentation, panoptic segmentation) and on different benchmarks (MS COCO, CityScapes, YoutubeVIS).
Semi-Parametric Inducing Point Networks and Neural Processes
Authors: Richa Rastogi; Yair Schiff; Alon Hacohen; Zhaozhi Li; Ian Lee; Yuntian Deng; Mert R. Sabuncu; Volodymyr Kuleshov
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner. Semi-parametric architectures are typically more compact than parametric models, but their computational complexity is often quadratic. In contrast, SPIN attains linear complexity via a cross-attention mechanism between datapoints inspired by inducing point methods. Querying large training sets can be particularly useful in meta-learning, as it unlocks additional training signal, but often exceeds the scaling limits of existing models. We use SPIN as the basis of the Inducing Point Neural Process, a probabilistic model which supports large contexts in meta-learning and achieves high accuracy where existing models fail. In our experiments, SPIN reduces memory requirements, improves accuracy across a range of meta-learning tasks, and improves state-of-the-art performance on an important practical problem, genotype imputation.
Countering the Attack-Defense Complexity Gap for Robust Classifiers
Authors: Samuele Marro; Michele Lombardi
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We consider the decision version of defending and attacking Machine Learning classifiers. We provide a rationale for the well-known difficulties in building robust models: in particular we prove that, under broad assumptions, attacking a polynomial-time classifier is $NP$-complete, while training a polynomial-time model that is robust on even a single input is $\Sigma_2^P$-complete. We also provide more general bounds for non-polynomial classifiers. We then show how such a complexity gap can be sidestepped by introducing Counter-Attack (CA), a system that computes on-the-fly robustness certificates for a given input up to an arbitrary distance bound $\varepsilon$. We also prove that, even when attacked with perturbations of magnitude $\varepsilon^\prime > \varepsilon$, CA still provides computational robustness: specifically, while computing a certificate is $NP$-complete, attacking the system beyond its intended robustness is $\Sigma_2^P$-complete. Since the exact form of CA can still be computationally expensive, we introduce a relaxation of this method, which we empirically show to be reliable at identifying non-robust inputs. As part of our work, we introduce UG100, a new dataset obtained by applying a provably optimal attack to six limited-scale networks (three for MNIST and three for CIFAR10), each trained in three different manners.
Dataless Knowledge Fusion by Merging Weights of Language Models
Authors: Xisen Jin; Xiang Ren; Daniel Preotiuc-Pietro; Pengxiang Cheng
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns. This creates a barrier to fusing knowledge across individual models to yield a better single model. In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data. We propose a data-less knowledge fusion method that merges models in their parameter space, guided by weights that minimize prediction differences between the merged model and the individual models. Over a battery of evaluation settings, we show that the proposed method significantly outperforms baselines such as Fisher-weighted averaging or model ensembling. Further, we find that our method is a promising alternative to multi-task learning that can preserve or sometimes improve over the individual models without access to the training data. Finally, model merging is more efficient than training a multi-task model, thus making it applicable to a wider set of scenarios.
Spectral Decomposition Representation for Reinforcement Learning
Authors: Tongzheng Ren; Tianjun Zhang; Lisa Lee; Joseph E. Gonzalez; Dale Schuurmans; Bo Dai
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Representation learning often plays a critical role in avoiding the curse of dimensionality in reinforcement learning. A representative class of algorithms exploits spectral decomposition of the stochastic transition dynamics to construct representations that enjoy strong theoretical properties in idealized settings. However, current spectral methods suffer from limited applicability because they are constructed for
state-only aggregation and are derived from a policy-dependent transition kernel, without considering the issue of exploration. To address these issues, we propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy, while also balancing the exploration-versus-exploitation trade-off during learning. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. In addition, an experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several RL benchmarks.
Causal Balancing for Domain Generalization
Authors: Xinyi Wang; Michael Saxon; Jiachen Li; Hongyang Zhang; Kun Zhang; William Yang Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. We propose a balanced mini-batch sampling strategy to transform a biased data distribution into a spurious-free balanced distribution, based on the invariance of the underlying causal mechanisms for the data generation process. We argue that the Bayes optimal classifiers trained on such balanced distribution are minimax optimal across a diverse enough environment space. We also provide an identifiability guarantee of the latent variable model of the proposed data generation process, when utilizing enough train environments. Experiments are conducted on DomainBed, demonstrating empirically that our method obtains the best performance across 20 baselines reported on the benchmark.
Neural Compositional Rule Learning for Knowledge Graph Reasoning
Authors: Kewei Cheng; Nesreen Ahmed; Yizhou Sun
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well as their ability to generalize to other tasks, domains, and data. While recent methods have been proposed to learn logical rules, the majority of these methods are either restricted by their computational complexity and can not handle the large search space of large-scale KGs, or show poor generalization when exposed to data outside the training set. In this paper, we propose an end-to-end neural model for learning compositional logical rules called NCRL. NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head. By recurrently merging compositions in the rule body with a recurrent attention unit, NCRL finally predicts a single rule head. Experimental results show that NCRL learns high-quality rules, as well as being generalizable. Specifically, we show that NCRL is scalable, efficient, and yields state-of-the-art results for knowledge graph completion on large-scale KGs. Moreover, we test NCRL for systematic generalization by learning to reason on small-scale observed graphs and evaluating on larger unseen ones.
Sampled Transformer for Point Sets
Authors: Shidi Li; Christian Walder; Alexander Soen; Lexing Xie; miaomiao Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The sparse transformer can reduce the computational complexity of the self-attention layers to $O(n)$, whilst still being a universal approximator of continuous sequence-to-sequence functions. However, this permutation variant operation is not appropriate for direct application to sets. In this paper, we proposed an $O(n)$ complexity sampled transformer that can process point set elements directly without any additional inductive bias. Our sampled transformer introduces random element sampling, which randomly splits point sets into subsets, followed by applying a shared Hamiltonian self-attention mechanism to each subset. The overall attention mechanism can be viewed as a Hamiltonian cycle in the complete attention graph, and the permutation of point set elements is equivalent to randomly sampling Hamiltonian cycles. This mechanism implements a Monte Carlo simulation of the $O(n^2)$ dense attention connections. We show that it is a universal approximator for continuous set-to-set functions.  Experimental results for classification and few-shot learning on point-clouds show comparable or better accuracy with significantly reduced computational complexity compared to the dense transformer or alternative sparse attention schemes.
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Authors: Anton Bakhtin; David J Wu; Adam Lerer; Jonathan Gray; Athul Paul Jacob; Gabriele Farina; Alexander H Miller; Noam Brown
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is insufficient for achieving optimal performance in domains involving cooperation with humans. We address this shortcoming by first introducing a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy. We prove that this is a no-regret learning algorithm under a modified utility function. We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while simultaneously training an agent that responds well to this human model. We used RL-DiL-piKL to train an agent we name Diplodocus.
In a 200-game no-press Diplomacy tournament involving 62 human participants spanning skill levels from beginner to expert, two Diplodocus agents both achieved a higher average score than all other participants who played more than two games, and ranked first and third according to an Elo ratings model.