Wang, Yisen

102 publications

NeurIPS 2025 $\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning Xiaojun Guo, Ang Li, Yifei Wang, Stefanie Jegelka, Yisen Wang

JMLR 2025 An Augmentation Overlap Theory of Contrastive Learning Qi Zhang, Yifei Wang, Yisen Wang

ICML 2025 An Augmentation-Aware Theory for Self-Supervised Contrastive Learning Jingyi Cui, Hongwei Wen, Yisen Wang

TMLR 2025 AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation Zijun Wang, Haoqin Tu, Jieru Mei, Bingchen Zhao, Yisen Wang, Cihang Xie

ICLR 2025 Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan, Qi Lei, Stefanie Jegelka, Yisen Wang

ICLR 2025 Can In-Context Learning Really Generalize to Out-of-Distribution Tasks? Qixun Wang, Yifei Wang, Xianghua Ying, Yisen Wang

NeurIPS 2025 Finding and Reactivating Post-Trained LLMs' Hidden Safety Mechanisms Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang, Yisen Wang

ICML 2025 Identifying and Understanding Cross-Class Features in Adversarial Training Zeming Wei, Steven Y. Guo, Yisen Wang

ICML 2025 Incorporating Arbitrary Matrix Group Equivariance into KANs Lexiang Hu, Yisen Wang, Zhouchen Lin

NeurIPS 2025 Language Ranker: A Lightweight Ranking Framework for LLM Decoding Chenheng Zhang, Tianqi Du, Jizhe Zhang, Mingqing Xiao, Yifei Wang, Yisen Wang, Zhouchen Lin

ICLR 2025 Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD Ze Peng, Jian Zhang, Yisen Wang, Lei Qi, Yinghuan Shi, Yang Gao

ICML 2025 Long-Short Alignment for Effective Long-Context Modeling in LLMs Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang

ICLR 2025 Projection Head Is Secretly an Information Bottleneck Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, Yisen Wang

ICLR 2025 Rethinking Invariance in In-Context Learning Lizhe Fang, Yifei Wang, Khashayar Gatmiry, Lei Fang, Yisen Wang

ICLR 2025 SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang, Yisen Wang

ICLR 2025 TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice Shen Yan, Xingyan Bin, Sijun Zhang, Yisen Wang, Zhouchen Lin

ICLR 2025 What Is Wrong with Perplexity for Long-Context Language Modeling? Lizhe Fang, Yifei Wang, Zhaoyang Liu, Chenheng Zhang, Stefanie Jegelka, Jinyang Gao, Bolin Ding, Yisen Wang

ICLRW 2025 When More Is Less: Understanding Chain-of-Thought Length in LLMs Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, Yisen Wang

NeurIPS 2024 A Canonicalization Perspective on Invariant and Equivariant Learning George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang

NeurIPS 2024 A Theoretical Understanding of Self-Correction Through In-Context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang

ICMLW 2024 A Theoretical Understanding of Self-Correction Through In-Context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang

ICMLW 2024 A Theoretical Understanding of Self-Correction Through In-Context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang

NeurIPS 2024 Dissecting the Failure of Invariant Learning on Graphs Qixun Wang, Yifei Wang, Yisen Wang, Xianghua Ying

ICLR 2024 Do Generated Data Always Help Contrastive Learning? Yifei Wang, Jizhe Zhang, Yisen Wang

NeurIPS 2024 Fight Back Against Jailbreaking via Prompt Adversarial Tuning Yichuan Mo, Yuji Wang, Zeming Wei, Yisen Wang

ACML 2024 Graph Neural Networks (with Proper Weights) Can Escape Oversmoothing Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang

ICLRW 2024 How to Craft Backdoors with Unlabeled Data Alone? Yifei Wang, Wenhan Ma, Stefanie Jegelka, Yisen Wang

ICML 2024 Look Ahead or Look Around? a Theoretical Comparison Between Autoregressive and Masked Pretraining Qi Zhang, Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang

ICLR 2024 Non-Negative Contrastive Learning Yifei Wang, Qi Zhang, Yaoyu Guo, Yisen Wang

ICLR 2024 On the Role of Discrete Tokenization in Visual Representation Learning Tianqi Du, Yifei Wang, Yisen Wang

ICML 2024 PID: Prompt-Independent Data Protection Against Latent Diffusion Models Ang Li, Yichuan Mo, Mingjie Li, Yisen Wang

ICMLW 2024 Rethinking Invariance in In-Context Learning Lizhe Fang, Yifei Wang, Khashayar Gatmiry, Lei Fang, Yisen Wang

ICML 2024 TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang

NeurIPS 2024 Understanding the Role of Equivariance in Self-Supervised Learning Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka

ICMLW 2024 Understanding the Role of Equivariance in Self-Supervised Learning Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka

ICLRW 2024 Virtual Classifier: A Reversed Approach for Robust Image Evaluation Jizhe Zhang, Yifei Wang, Yisen Wang

ICLR 2023 A Message Passing Perspective on Learning Dynamics of Contrastive Learning Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang

NeurIPS 2023 Adversarial Examples Are Not Real Features Ang Li, Yifei Wang, Yiwen Guo, Yisen Wang

ICLR 2023 ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, Weiran Huang

NeurIPS 2023 Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning Xiaojun Guo, Yifei Wang, Zeming Wei, Yisen Wang

NeurIPS 2023 Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective Yifei Wang, Liangchen Li, Jiansheng Yang, Zhouchen Lin, Yisen Wang

CVPR 2023 CFA: Class-Wise Calibrated Fair Adversarial Training Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang

ICLR 2023 ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang

NeurIPS 2023 GEQ: Gaussian Kernel Inspired Equilibrium Models Mingjie Li, Yisen Wang, Zhouchen Lin

CVPR 2023 Generalist: Decoupling Natural and Robust Generalization Hongjun Wang, Yisen Wang

NeurIPS 2023 Identifiable Contrastive Learning with Automatic Feature Importance Discovery Qi Zhang, Yifei Wang, Yisen Wang

NeurIPS 2023 Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding George Ma, Yifei Wang, Yisen Wang

AAAI 2023 On the Connection Between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization Shiji Xin, Yifei Wang, Jingtong Su, Yisen Wang

ICML 2023 On the Generalization of Multi-Modal Contrastive Learning Qi Zhang, Yifei Wang, Yisen Wang

ICML 2023 Rethinking Weak Supervision in Helping Contrastive Learning Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang

ICLR 2023 Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning Rundong Luo, Yifei Wang, Yisen Wang

ICLRW 2023 Rethinking the Necessity of Labels in Backdoor Removal Zidi Xiong, Dongxian Wu, Yifei Wang, Yisen Wang

ICCV 2023 Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo

ICLR 2023 Towards a Unified Theoretical Understanding of Non-Contrastive Learning via Rank Differential Mechanism Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang

ICLR 2023 Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States Mingjie Li, Yifei Wang, Yisen Wang, Zhouchen Lin

ICLRW 2023 What Contrastive Learning Learns Beyond Class-Wise Features? Xingyuming Liu, Yifei Wang, Yisen Wang

ICLR 2022 A Unified Contrastive Energy-Based Model for Understanding the Generative Ability of Adversarial Training Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

ICML 2022 CerDEQ: Certifiable Deep Equilibrium Model Mingjie Li, Yisen Wang, Zhouchen Lin

ICML 2022 Certified Adversarial Robustness Under the Bounded Support Set Yiwen Kou, Qinyuan Zheng, Yisen Wang

ICLR 2022 Chaos Is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

ICML 2022 G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin

NeurIPS 2022 How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders Qi Zhang, Yifei Wang, Yisen Wang

NeurIPS 2022 Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors Qixun Wang, Yifei Wang, Hong Zhu, Yisen Wang

ICLR 2022 Optimization Inspired Multi-Branch Equilibrium Models Mingjie Li, Yisen Wang, Xingyu Xie, Zhouchen Lin

ICML 2022 Optimization-Induced Graph Implicit Nonlinear Diffusion Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

ICLR 2022 Self-Ensemble Adversarial Training for Improved Robustness Hongjun Wang, Yisen Wang

CVPR 2022 Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo

NeurIPS 2022 When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang

ICLR 2021 A Unified Approach to Interpreting and Boosting Adversarial Transferability Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang

ICMLW 2021 Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions Nodens Koren, Xingjun Ma, Qiuhong Ke, Yisen Wang, James Bailey

NeurIPS 2021 Adversarial Neuron Pruning Purifies Backdoored Deep Models Dongxian Wu, Yisen Wang

ICML 2021 Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

NeurIPS 2021 Clustering Effect of Adversarial Robust Models Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang

ICMLW 2021 Demystifying Adversarial Training via a Unified Probabilistic Framework Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

NeurIPS 2021 Dissecting the Diffusion Process in Linear Graph Convolutional Networks Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

NeurIPS 2021 Efficient Equivariant Network Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin

NeurIPS 2021 Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang, Yisen Wang, Sarah Erfani, Quanquan Gu, James Bailey, Xingjun Ma

NeurIPS 2021 Finding Optimal Tangent Points for Reducing Distortions of Hard-Label Attacks Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang

ICML 2021 GBHT: Gradient Boosting Histogram Transform for Density Estimation Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin

NeurIPS 2021 Gauge Equivariant Transformer Lingshen He, Yiming Dong, Yisen Wang, Dacheng Tao, Zhouchen Lin

ICLR 2021 Improving Adversarial Robustness via Channel-Wise Activation Suppressing Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang

ICML 2021 Leveraged Weighted Loss for Partial Label Learning Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin

NeurIPS 2021 Morié Attack (MA): A New Potential Risk of Screen Photos Dantong Niu, Ruohao Guo, Yisen Wang

NeurIPS 2021 On Training Implicit Models Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin

ECML-PKDD 2021 Reparameterized Sampling for Generative Adversarial Networks Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

NeurIPS 2021 Residual Relaxation for Multi-View Representation Learning Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin

NeurIPS 2021 Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang

NeurIPS 2021 Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin

ICLR 2021 Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang

NeurIPS 2020 Adversarial Weight Perturbation Helps Robust Generalization Dongxian Wu, Shu-Tao Xia, Yisen Wang

ICLR 2020 Improving Adversarial Robustness Requires Revisiting Misclassified Examples Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu

ECCV 2020 Improving Query Efficiency of Black-Box Adversarial Attack Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo

ICML 2020 Normalized Loss Functions for Deep Learning with Noisy Labels Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, James Bailey

ICLR 2020 Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, Xingjun Ma

ICML 2019 On the Convergence and Robustness of Adversarial Training Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu

ICLR 2018 Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey

ICML 2018 Dimensionality-Driven Learning with Noisy Labels Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, James Bailey

UAI 2018 Learning Deep Hidden Nonlinear Dynamics from Aggregate Data Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha

IJCAI 2017 Robust Survey Aggregation with Student-T Distribution and Sparse Representation Qingtao Tang, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, Jianfei Cai

IJCAI 2017 Student-T Process Regression with Student-T Likelihood Qingtao Tang, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, Shu-Tao Xia

AAAI 2017 Unbiased Multivariate Correlation Analysis Yisen Wang, Simone Romano, Vinh Nguyen, James Bailey, Xingjun Ma, Shu-Tao Xia

IJCAI 2016 Bernoulli Random Forests: Closing the Gap Between Theoretical Consistency and Empirical Soundness Yisen Wang, Qingtao Tang, Shu-Tao Xia, Jia Wu, Xingquan Zhu