| 2026 | ISSAC | On the Summability Problem of Multivariate Rational Functions in the Mixed Case. | Shaoshi Chen, Lixin Du, Hanqian Fang, Yisen Wang |
| 2025 | ICLR | Can In-context Learning Really Generalize to Out-of-distribution Tasks? | Qixun Wang, Yifei Wang, Xianghua Ying, Yisen Wang |
| 2025 | ICLR | Rethinking Invariance in In-context Learning. | Lizhe Fang, Yifei Wang, Khashayar Gatmiry, Lei Fang, Yisen Wang |
| 2025 | ICLR | 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 |
| 2025 | ICLR | SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation. | Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang, Yisen Wang |
| 2025 | ICLR | Projection Head is Secretly an Information Bottleneck. | Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, Yisen Wang |
| 2025 | ICLR | Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD. | Ze Peng, Jian Zhang, Yisen Wang, Lei Qi, Yinghuan Shi, Yang Gao |
| 2025 | ICLR | TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice. | Shen Yan, Xingyan Bin, Sijun Zhang, Yisen Wang, Zhouchen Lin |
| 2025 | ICLR | Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness. | Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan, Qi Lei, Stefanie Jegelka, Yisen Wang |
| 2025 | ICML | An Augmentation-Aware Theory for Self-Supervised Contrastive Learning. | Jingyi Cui, Hongwei Wen, Yisen Wang |
| 2025 | ICML | Long-Short Alignment for Effective Long-Context Modeling in LLMs. | Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang |
| 2025 | ICML | Incorporating Arbitrary Matrix Group Equivariance into KANs. | Lexiang Hu, Yisen Wang, Zhouchen Lin |
| 2025 | ICML | Identifying and Understanding Cross-Class Features in Adversarial Training. | Zeming Wei, Steven Y. Guo, Yisen Wang |
| 2025 | ISSAC | Non-minimality of minimal telescopers explained by residues. | Shaoshi Chen, Manuel Kauers, Christoph Koutschan, Xiuyun Li, Rong-Hua Wang, Yisen Wang |
| 2025 | SMC | Intelligent Extraction Technology of Security Attributes for Chip Manual. | Yudong Huang, Yisen Wang, Siyuan Liang, Tianchan Yang, Yaxuan Feng |
| 2025 | TrustCom | MR-Patch: A Retrieval-Augmented Generation Approach for Patch Presence Test. | Zirui Jiang, Yisen Wang, Xingyu Bai, Jiajun Du, Tianchan Yang, Bing Zhu |
| 2024 | ACML | Graph Neural Networks (with Proper Weights) Can Escape Oversmoothing. | Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang |
| 2024 | ACSSC | Three-Dimensional Signal Processing: A New Approach in Dynamical Sampling Via Tensor Products. | Yisen Wang, HanQin Cal, Longxiu Huang |
| 2024 | ICLR | On the Role of Discrete Tokenization in Visual Representation Learning. | Tianqi Du, Yifei Wang, Yisen Wang |
| 2024 | ICLR | Do Generated Data Always Help Contrastive Learning? | Yifei Wang, Jizhe Zhang, Yisen Wang |
| 2024 | ICLR | Non-negative Contrastive Learning. | Yifei Wang, Qi Zhang, Yaoyu Guo, Yisen Wang |
| 2024 | ICML | PID: Prompt-Independent Data Protection Against Latent Diffusion Models. | Ang Li, Yichuan Mo, Mingjie Li, Yisen Wang |
| 2024 | ICML | TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors. | Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang |
| 2024 | ICML | Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining. | Qi Zhang, Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang |
| 2023 | AAAI | On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization. | Shiji Xin, Yifei Wang, Jingtong Su, Yisen Wang |
| 2023 | CVPR | Generalist: Decoupling Natural and Robust Generalization. | Hongjun Wang, Yisen Wang |
| 2023 | CVPR | CFA: Class-Wise Calibrated Fair Adversarial Training. | Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang |
| 2023 | ICCV | Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks. | Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo |
| 2023 | ICLR | A Message Passing Perspective on Learning Dynamics of Contrastive Learning. | Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang |
| 2023 | ICLR | ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond. | Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang |
| 2023 | ICLR | Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States. | Mingjie Li, Yifei Wang, Yisen Wang, Zhouchen Lin |
| 2023 | ICLR | Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. | Rundong Luo, Yifei Wang, Yisen Wang |
| 2023 | ICLR | ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations. | Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, Weiran Huang |
| 2023 | ICLR | Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism. | Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang |
| 2023 | ICML | Rethinking Weak Supervision in Helping Contrastive Learning. | Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang |
| 2023 | ICML | On the Generalization of Multi-modal Contrastive Learning. | Qi Zhang, Yifei Wang, Yisen Wang |
| 2022 | CVPR | 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 |
| 2022 | ICLR | A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. | Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin |
| 2022 | ICLR | Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap. | Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, Zhouchen Lin |
| 2022 | ICLR | Optimization inspired Multi-Branch Equilibrium Models. | Mingjie Li, Yisen Wang, Xingyu Xie, Zhouchen Lin |
| 2022 | ICLR | Self-ensemble Adversarial Training for Improved Robustness. | Hongjun Wang, Yisen Wang |
| 2022 | ICML | Optimization-Induced Graph Implicit Nonlinear Diffusion. | Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin |
| 2022 | ICML | Certified Adversarial Robustness Under the Bounded Support Set. | Yiwen Kou, Qinyuan Zheng, Yisen Wang |
| 2022 | ICML | CerDEQ: Certifiable Deep Equilibrium Model. | Mingjie Li, Yisen Wang, Zhouchen Lin |
| 2022 | ICML | G | Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin |
| 2021 | ICLR | Improving Adversarial Robustness via Channel-wise Activation Suppressing. | Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang |
| 2021 | ICLR | Unlearnable Examples: Making Personal Data Unexploitable. | Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang |
| 2021 | ICLR | A Unified Approach to Interpreting and Boosting Adversarial Transferability. | Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang |
| 2021 | ICML | GBHT: Gradient Boosting Histogram Transform for Density Estimation. | Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin |
| 2021 | ICML | Leveraged Weighted Loss for Partial Label Learning. | Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin |
| 2021 | ICML | Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? | Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville |
| 2021 | KDD | Analysis and Applications of Class-wise Robustness in Adversarial Training. | Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, Yisen Wang |
| 2020 | CVPR | Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles. | Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A. K. Qin, Yun Yang |
| 2020 | ECCV | Improving Query Efficiency of Black-Box Adversarial Attack. | Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo |
| 2020 | ICLR | Improving Adversarial Robustness Requires Revisiting Misclassified Examples. | Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu |
| 2020 | ICLR | Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. | Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, Xingjun Ma |
| 2020 | ICML | Normalized Loss Functions for Deep Learning with Noisy Labels. | Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah M. Erfani, James Bailey |
| 2020 | ICPR | Improving Gravitational Wave Detection with 2D Convolutional Neural Networks. | Siyu Fan, Yisen Wang, Yuan Luo, Alexander Schmitt, Shenghua Yu |
| 2020 | IJCNN | Temporal Calibrated Regularization for Robust Noisy Label Learning. | Dongxian Wu, Yisen Wang, Zhuobin Zheng, Shu-Tao Xia |
| 2019 | EMNLP | Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation. | Min Zeng, Yisen Wang, Yuan Luo |
| 2019 | ICCV | Symmetric Cross Entropy for Robust Learning With Noisy Labels. | Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey |
| 2019 | ICCV | Hilbert-Based Generative Defense for Adversarial Examples. | Yang Bai, Yan Feng, Yisen Wang, Tao Dai, Shutao Xia, Yong Jiang |
| 2019 | ICML | On the Convergence and Robustness of Adversarial Training. | Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu |
| 2018 | CVPR | Decoupled Networks. | Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song |
| 2018 | CVPR | Iterative Learning With Open-Set Noisy Labels. | Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia |
| 2018 | ICLR | Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. | Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey |
| 2018 | ICML | Dimensionality-Driven Learning with Noisy Labels. | Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi N. R. Wijewickrema, James Bailey |
| 2018 | UAI | Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. | Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha |
| 2017 | AAAI | Unbiased Multivariate Correlation Analysis. | Yisen Wang, Simone Romano, Vinh Nguyen, James Bailey, Xingjun Ma, Shu-Tao Xia |
| 2017 | ICASSP | Unifying attribute splitting criteria of decision trees by Tsallis entropy. | Yisen Wang, Shu-Tao Xia |
| 2017 | IJCAI | Robust Survey Aggregation with Student-t Distribution and Sparse Representation. | Qingtao Tang, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, Jianfei Cai |
| 2017 | IJCAI | Student-t Process Regression with Student-t Likelihood. | Qingtao Tang, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, Shu-Tao Xia |
| 2016 | ECAI | Student-t Process Regression with Dependent Student-t Noise. | Qingtao Tang, Yisen Wang, Shu-Tao Xia |
| 2016 | IJCAI | Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness. | Yisen Wang, Qingtao Tang, Shu-Tao Xia, Jia Wu, Xingquan Zhu |
| 2016 | IJCNN | Improving decision trees by Tsallis Entropy Information Metric method. | Yisen Wang, Chao-Bing Song, Shu-Tao Xia |
| 2016 | IJCNN | A novel feature subspace selection method in random forests for high dimensional data. | Yisen Wang, Shu-Tao Xia |