Improving Decision Trees Using Tsallis Entropy. - Yisen Wang 001; Chao-Bing Song; Shu-Tao Xia (CoRR, 2015)

Student-t Process Regression with Dependent Student-t Noise. - Qingtao Tang; Yisen Wang 001; Shu-Tao Xia (ECAI, 2016)

Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness. - Yisen Wang 001; Qingtao Tang; Shu-Tao Xia; Jia Wu 001; Xingquan Zhu 001 (IJCAI, 2016)

Improving decision trees by Tsallis Entropy Information Metric method. - Yisen Wang 001; Chao-Bing Song; Shu-Tao Xia (IJCNN, 2016)

A novel feature subspace selection method in random forests for high dimensional data. - Yisen Wang 001; Shu-Tao Xia (IJCNN, 2016)

End-to-end coding for TCP. - Yong Cui 001; Lian Wang; Xin Wang 001; Yisen Wang 001; Fengyuan Ren; Shu-Tao Xia (IEEE Netw., 2016)

Unbiased Multivariate Correlation Analysis. - Yisen Wang 001; Simone Romano 003; Vinh Nguyen; James Bailey 001; Xingjun Ma; Shu-Tao Xia (AAAI, 2017)

Unifying attribute splitting criteria of decision trees by Tsallis entropy. - Yisen Wang 001; Shu-Tao Xia (ICASSP, 2017)

Robust Survey Aggregation with Student-t Distribution and Sparse Representation. - Qingtao Tang; Tao Dai 001; Li Niu 002; Yisen Wang 001; Shu-Tao Xia; Jianfei Cai 001 (IJCAI, 2017)

Student-t Process Regression with Student-t Likelihood. - Qingtao Tang; Li Niu 002; Yisen Wang 001; Tao Dai 001; Wangpeng An; Jianfei Cai 001; Shu-Tao Xia (IJCAI, 2017)

Residual Convolutional CTC Networks for Automatic Speech Recognition. - Yisen Wang 001; Xuejiao Deng; Songbai Pu; Zhiheng Huang (CoRR, 2017)

Link sign prediction by Variational Bayesian Probabilistic Matrix Factorization with Student-t Prior. - Yisen Wang 001; Fangbing Liu; Shu-Tao Xia; Jia Wu 001 (Inf. Sci., 2017)

A generic denoising framework via guided principal component analysis. - Tao Dai 001; Zhiya Xu; Haoyi Liang; Ke Gu 001; Qingtao Tang; Yisen Wang 001; Weizhi Lu; Shu-Tao Xia (J. Vis. Commun. Image Represent., 2017)

A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification. - Yisen Wang 001; Shu-Tao Xia; Jia Wu 001 (Knowl. Based Syst., 2017)

Decoupled Networks. - Weiyang Liu; Zhen Liu 019; Zhiding Yu; Bo Dai 001; Rongmei Lin; Yisen Wang 001; James M. Rehg; Le Song (CVPR, 2018)

Iterative Learning With Open-Set Noisy Labels. - Yisen Wang 001; Weiyang Liu; Xingjun Ma; James Bailey 001; Hongyuan Zha; Le Song; Shu-Tao Xia (CVPR, 2018)

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. - Xingjun Ma; Bo Li 026; Yisen Wang 001; Sarah M. Erfani; Sudanthi N. R. Wijewickrema; Grant Schoenebeck; Dawn Song; Michael E. Houle; James Bailey 001 (ICLR, 2018)

Dimensionality-Driven Learning with Noisy Labels. - Xingjun Ma; Yisen Wang 001; Michael E. Houle; Shuo Zhou 001; Sarah M. Erfani; Shu-Tao Xia; Sudanthi N. R. Wijewickrema; James Bailey 001 (ICML, 2018)

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. - Yisen Wang 001; Bo Dai 001; Lingkai Kong; Sarah Monazam Erfani; James Bailey 001; Hongyuan Zha (UAI, 2018)

A Novel Consistent Random Forest Framework: Bernoulli Random Forests. - Yisen Wang 001; Shu-Tao Xia; Qingtao Tang; Jia Wu 001; Xingquan Zhu 001 (IEEE Trans. Neural Networks Learn. Syst., 2018)

Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation. - Min Zeng; Yisen Wang 001; Yuan Luo 003 (EMNLP/IJCNLP (1), 2019)

Symmetric Cross Entropy for Robust Learning With Noisy Labels. - Yisen Wang 001; Xingjun Ma; Zaiyi Chen; Yuan Luo 003; Jinfeng Yi; James Bailey 001 (ICCV, 2019)

Hilbert-Based Generative Defense for Adversarial Examples. - Yang Bai; Yan Feng; Yisen Wang 001; Tao Dai 001; Shutao Xia; Yong Jiang 001 (ICCV, 2019)

Self-Attentive Networks for one-shot Image Recognition. - Pin Fang; Yisen Wang 001; Yuan Luo 003 (ICME, 2019)

On the Convergence and Robustness of Adversarial Training. - Yisen Wang 001; Xingjun Ma; James Bailey 001; Jinfeng Yi; Bowen Zhou 001; Quanquan Gu (ICML, 2019)

Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation. - Dongdong Chen; Yisen Wang 001; Jinfeng Yi; Zaiyi Chen; Zhi-Hua Zhou (CoRR, 2019)

Collaborative Representation Cascade for Single-Image Super-Resolution. - Yongbing Zhang 002; Yulun Zhang 001; Jian Zhang 018; Dong Xu 001; Yun Fu 001; Yisen Wang 001; Xiangyang Ji; Qionghai Dai (IEEE Trans. Syst. Man Cybern. Syst., 2019)

Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles. - Ranjie Duan; Xingjun Ma; Yisen Wang 001; James Bailey 001; A. Kai Qin; Yun Yang 001 (CVPR, 2020)

Improving Query Efficiency of Black-Box Adversarial Attack. - Yang Bai; Yuyuan Zeng; Yong Jiang 001; Yisen Wang 001; Shu-Tao Xia; Weiwei Guo (ECCV (25), 2020)

Improving Adversarial Robustness Requires Revisiting Misclassified Examples. - Yisen Wang 001; Difan Zou; Jinfeng Yi; James Bailey 001; Xingjun Ma; Quanquan Gu (ICLR, 2020)

Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. - Dongxian Wu; Yisen Wang 001; Shu-Tao Xia; James Bailey 001; Xingjun Ma (ICLR, 2020)

Normalized Loss Functions for Deep Learning with Noisy Labels. - Xingjun Ma; Hanxun Huang; Yisen Wang 001; Simone Romano 003; Sarah M. Erfani; James Bailey 001 (ICML, 2020)

Improving Gravitational Wave Detection with 2D Convolutional Neural Networks. - Siyu Fan; Yisen Wang 001; Yuan Luo 003; Alexander Schmitt; Shenghua Yu (ICPR, 2020)

Temporal Calibrated Regularization for Robust Noisy Label Learning. - Dongxian Wu; Yisen Wang 001; Zhuobin Zheng; Shu-Tao Xia (IJCNN, 2020)

Adversarial Weight Perturbation Helps Robust Generalization. - Dongxian Wu; Shu-Tao Xia; Yisen Wang 001 (NeurIPS, 2020)

Revisiting Loss Landscape for Adversarial Robustness. - Dongxian Wu; Yisen Wang 001; Shutao Xia (CoRR, 2020)

Improving Adversarial Robustness via Channel-wise Activation Suppressing. - Yang Bai; Yuyuan Zeng; Yong Jiang 001; Shu-Tao Xia; Xingjun Ma; Yisen Wang 001 (ICLR, 2021)

Unlearnable Examples: Making Personal Data Unexploitable. - Hanxun Huang; Xingjun Ma; Sarah Monazam Erfani; James Bailey 001; Yisen Wang 001 (ICLR, 2021)

A Unified Approach to Interpreting and Boosting Adversarial Transferability. - Xin Wang 108; Jie Ren 018; Shuyun Lin; Xiangming Zhu 002; Yisen Wang 001; Quanshi Zhang (ICLR, 2021)

GBHT: Gradient Boosting Histogram Transform for Density Estimation. - Jingyi Cui; Hanyuan Hang; Yisen Wang 001; Zhouchen Lin (ICML, 2021)

Leveraged Weighted Loss for Partial Label Learning. - Hongwei Wen; Jingyi Cui; Hanyuan Hang; Jiabin Liu; Yisen Wang 001; Zhouchen Lin (ICML, 2021)

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? - Dinghuai Zhang; Kartik Ahuja; Yilun Xu; Yisen Wang 001; Aaron C. Courville (ICML, 2021)

Analysis and Applications of Class-wise Robustness in Adversarial Training. - Qi Tian 001; Kun Kuang; Kelu Jiang; Fei Wu 001; Yisen Wang 001 (KDD, 2021)

Clustering Effect of Adversarial Robust Models. - Yang Bai; Xin Yan; Yong Jiang 001; Shu-Tao Xia; Yisen Wang 001 (NeurIPS, 2021)

On Training Implicit Models. - Zhengyang Geng; Xin-Yu Zhang; Shaojie Bai; Yisen Wang 001; Zhouchen Lin (NeurIPS, 2021)

Efficient Equivariant Network. - Lingshen He; Yuxuan Chen; Zhengyang Shen; Yiming Dong; Yisen Wang 001; Zhouchen Lin (NeurIPS, 2021)

Gauge Equivariant Transformer. - Lingshen He; Yiming Dong; Yisen Wang 001; Dacheng Tao; Zhouchen Lin (NeurIPS, 2021)

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. - Hanxun Huang; Yisen Wang 001; Sarah M. Erfani; Quanquan Gu; James Bailey 001; Xingjun Ma (NeurIPS, 2021)

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks. - Chen Ma 003; Xiangyu Guo; Li Chen 031; Jun-Hai Yong; Yisen Wang 001 (NeurIPS, 2021)

Morié Attack (MA): A New Potential Risk of Screen Photos. - Dantong Niu; Ruohao Guo; Yisen Wang 001 (NeurIPS, 2021)

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Yiting Chen 003; Xu Cheng 005; Xin Wang 108; Meng Zhou; Jie Shi; Quanshi Zhang (NeurIPS, 2021)

Residual Relaxation for Multi-view Representation Learning. - Yifei Wang 001; Zhengyang Geng; Feng Jiang; Chuming Li; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (NeurIPS, 2021)

Dissecting the Diffusion Process in Linear Graph Convolutional Networks. - Yifei Wang 001; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (NeurIPS, 2021)

Adversarial Neuron Pruning Purifies Backdoored Deep Models. - Dongxian Wu; Yisen Wang 001 (NeurIPS, 2021)

Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. - Mingqing Xiao 002; Qingyan Meng; Zongpeng Zhang; Yisen Wang 001; Zhouchen Lin (NeurIPS, 2021)

Reparameterized Sampling for Generative Adversarial Networks. - Yifei Wang 001; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (ECML/PKDD (3), 2021)

Improving Generalization of Deep Reinforcement Learning-based TSP Solvers. - Wenbin Ouyang; Yisen Wang 001; Shaochen Han; Zhejian Jin; Paul Weng (SSCI, 2021)

Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions. - Nodens Koren; Qiuhong Ke; Yisen Wang 001; James Bailey 001; Xingjun Ma (CoRR, 2021)

What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space. - Shihao Zhao; Xingjun Ma; Yisen Wang 001; James Bailey 001; Bo Li 026; Yu-Gang Jiang 001 (CoRR, 2021)

Game-theoretic Understanding of Adversarially Learned Features. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Xu Cheng 005; Xin Wang 108; Yiting Chen 003; Jie Shi; Quanshi Zhang (CoRR, 2021)

Optimization Induced Equilibrium Networks. - Xingyu Xie; Qiuhao Wang; Zenan Ling; Xia Li 005; Yisen Wang 001; Guangcan Liu; Zhouchen Lin (CoRR, 2021)

Moiré Attack (MA): A New Potential Risk of Screen Photos. - Dantong Niu; Ruohao Guo; Yisen Wang 001 (CoRR, 2021)

Fooling Adversarial Training with Inducing Noise. - Zhirui Wang; Yifei Wang 001; Yisen Wang 001 (CoRR, 2021)

Clustering Effect of (Linearized) Adversarial Robust Models. - Yang Bai; Xin Yan; Yong Jiang 001; Shu-Tao Xia; Yisen Wang 001 (CoRR, 2021)

Understanding adversarial attacks on deep learning based medical image analysis systems. - Xingjun Ma; Yuhao Niu; Lin Gu 003; Yisen Wang 001; Yitian Zhao; James Bailey 001; Feng Lu 005 (Pattern Recognit., 2021)

Efficient and Scalable Implicit Graph Neural Networks with Virtual Equilibrium. - Qi Chen; Yifei Wang 001; Yisen Wang 001; Jianlong Chang; Qi Tian 001; Jiansheng Yang; Zhouchen Lin (IEEE Big Data, 2022)

Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. - Qingyan Meng; Mingqing Xiao 002; Shen Yan 004; Yisen Wang 001; Zhouchen Lin; Zhi-Quan Luo (CVPR, 2022)

A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. - Yifei Wang 001; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (ICLR, 2022)

Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap. - Yifei Wang 001; Qi Zhang 067; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (ICLR, 2022)

Optimization inspired Multi-Branch Equilibrium Models. - Mingjie Li 007; Yisen Wang 001; Xingyu Xie; Zhouchen Lin (ICLR, 2022)

Self-ensemble Adversarial Training for Improved Robustness. - Hongjun Wang; Yisen Wang 001 (ICLR, 2022)

Optimization-Induced Graph Implicit Nonlinear Diffusion. - Qi Chen; Yifei Wang 001; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (ICML, 2022)

Certified Adversarial Robustness Under the Bounded Support Set. - Yiwen Kou; Qinyuan Zheng; Yisen Wang 001 (ICML, 2022)

CerDEQ: Certifiable Deep Equilibrium Model. - Mingjie Li 007; Yisen Wang 001; Zhouchen Lin (ICML, 2022)

G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters. - Mingjie Li 007; Xiaojun Guo; Yifei Wang 001; Yisen Wang 001; Zhouchen Lin (ICML, 2022)

When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture. - Yichuan Mo; Dongxian Wu; Yifei Wang 001; Yiwen Guo; Yisen Wang 001 (NeurIPS, 2022)

Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors. - Qixun Wang 002; Yifei Wang 001; Hong Zhu 003; Yisen Wang 001 (NeurIPS, 2022)

How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders. - Qi Zhang 067; Yifei Wang 001; Yisen Wang 001 (NeurIPS, 2022)

Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability. - Quanshi Zhang; Xin Wang 108; Jie Ren 018; Xu Cheng 005; Shuyun Lin; Yisen Wang 001; Xiangming Zhu 002 (CoRR, 2022)

Training much deeper spiking neural networks with a small number of time-steps. - Qingyan Meng; Shen Yan 004; Mingqing Xiao 002; Yisen Wang 001; Zhouchen Lin; Zhi-Quan Luo (Neural Networks, 2022)

On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization. - Shiji Xin; Yifei Wang 001; Jingtong Su; Yisen Wang 001 (AAAI, 2023)

Generalist: Decoupling Natural and Robust Generalization. - Hongjun Wang; Yisen Wang 001 (CVPR, 2023)

CFA: Class-Wise Calibrated Fair Adversarial Training. - Zeming Wei; Yifei Wang 001; Yiwen Guo; Yisen Wang 001 (CVPR, 2023)

Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks. - Qingyan Meng; Mingqing Xiao 002; Shen Yan 004; Yisen Wang 001; Zhouchen Lin; Zhi-Quan Luo (ICCV, 2023)

A Message Passing Perspective on Learning Dynamics of Contrastive Learning. - Yifei Wang 001; Qi Zhang 067; Tianqi Du; Jiansheng Yang; Zhouchen Lin; Yisen Wang 001 (ICLR, 2023)

ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond. - Xiaojun Guo; Yifei Wang 001; Tianqi Du; Yisen Wang 001 (ICLR, 2023)

Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States. - Mingjie Li 007; Yifei Wang 001; Yisen Wang 001; Zhouchen Lin (ICLR, 2023)

Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. - Rundong Luo; Yifei Wang 001; Yisen Wang 001 (ICLR, 2023)

ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations. - Xuyang Zhao; Tianqi Du; Yisen Wang 001; Jun Yao; Weiran Huang 001 (ICLR, 2023)

Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism. - Zhijian Zhuo 001; Yifei Wang 001; Jinwen Ma; Yisen Wang 001 (ICLR, 2023)

Rethinking Weak Supervision in Helping Contrastive Learning. - Jingyi Cui; Weiran Huang 001; Yifei Wang 001; Yisen Wang 001 (ICML, 2023)

On the Generalization of Multi-modal Contrastive Learning. - Qi Zhang 067; Yifei Wang 001; Yisen Wang 001 (ICML, 2023)

Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning. - Xiaojun Guo; Yifei Wang 001; Zeming Wei; Yisen Wang 001 (NeurIPS, 2023)

GEQ: Gaussian Kernel Inspired Equilibrium Models. - Mingjie Li 007; Yisen Wang 001; Zhouchen Lin (NeurIPS, 2023)

Adversarial Examples Are Not Real Features. - Ang Li; Yifei Wang 001; Yiwen Guo; Yisen Wang 001 (NeurIPS, 2023)

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. - George Ma; Yifei Wang 001; Yisen Wang 001 (NeurIPS, 2023)

Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective. - Yifei Wang 001; Liangchen Li; Jiansheng Yang; Zhouchen Lin; Yisen Wang 001 (NeurIPS, 2023)

Identifiable Contrastive Learning with Automatic Feature Importance Discovery. - Qi Zhang 067; Yifei Wang 001; Yisen Wang 001 (NeurIPS, 2023)

Robust Long-Tailed Learning via Label-Aware Bounded CVaR. - Hong Zhu 003; Runpeng Yu 001; Xing Tang 007; Yifei Wang 001; Yuan Fang; Yisen Wang 001 (CoRR, 2023)

Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations. - Zeming Wei; Yifei Wang 001; Yisen Wang 001 (CoRR, 2023)

Towards Control-Centric Representations in Reinforcement Learning from Images. - Chen Liu; Hongyu Zang; Xin Li 033; Yong Heng; Yifei Wang 001; Zhen Fang; Yisen Wang 001; Mingzhong Wang (CoRR, 2023)

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. - Jiangyan Ma; Yifei Wang 001; Yisen Wang 001 (CoRR, 2023)

Query efficient black-box adversarial attack on deep neural networks. - Yang Bai; Yisen Wang 001; Yuyuan Zeng; Yong Jiang 001; Shu-Tao Xia (Pattern Recognit., 2023)

Equilibrium Image Denoising With Implicit Differentiation. - Qi Chen; Yifei Wang 001; Zhengyang Geng; Yisen Wang 001; Jiansheng Yang; Zhouchen Lin (IEEE Trans. Image Process., 2023)

SPIDE: A purely spike-based method for training feedback spiking neural networks. - Mingqing Xiao 002; Qingyan Meng; Zongpeng Zhang; Yisen Wang 001; Zhouchen Lin (Neural Networks, 2023)

Graph Neural Networks (with Proper Weights) Can Escape Oversmoothing. - Zhijian Zhuo 001; Yifei Wang 001; Jinwen Ma; Yisen Wang 001 (ACML, 2024)

On the Role of Discrete Tokenization in Visual Representation Learning. - Tianqi Du; Yifei Wang 001; Yisen Wang 001 (ICLR, 2024)

Do Generated Data Always Help Contrastive Learning? - Yifei Wang 001; Jizhe Zhang; Yisen Wang 001 (ICLR, 2024)

Non-negative Contrastive Learning. - Yifei Wang 001; Qi Zhang 067; Yaoyu Guo; Yisen Wang 001 (ICLR, 2024)

PID: Prompt-Independent Data Protection Against Latent Diffusion Models. - Ang Li; Yichuan Mo; Mingjie Li 007; Yisen Wang 001 (ICML, 2024)

TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors. - Yichuan Mo; Hui Huang; Mingjie Li 007; Ang Li; Yisen Wang 001 (ICML, 2024)

Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining. - Qi Zhang 067; Tianqi Du; Haotian Huang; Yifei Wang 001; Yisen Wang 001 (ICML, 2024)

Understanding the Role of Equivariance in Self-supervised Learning. - Yifei Wang 001; Kaiwen Hu; Sharut Gupta; Ziyu Ye; Yisen Wang 001; Stefanie Jegelka (NeurIPS, 2024)

A Theoretical Understanding of Self-Correction through In-context Alignment. - Yifei Wang 001; Yuyang Wu; Zeming Wei; Stefanie Jegelka; Yisen Wang 001 (NeurIPS, 2024)

Dissecting the Failure of Invariant Learning on Graphs. - Qixun Wang 002; Yifei Wang 001; Yisen Wang 001; Xianghua Ying (NeurIPS, 2024)

A Canonicalization Perspective on Invariant and Equivariant Learning. - George Ma; Yifei Wang 001; Derek Lim; Stefanie Jegelka; Yisen Wang 001 (NeurIPS, 2024)

Fight Back Against Jailbreaking via Prompt Adversarial Tuning. - Yichuan Mo; Yuji Wang; Zeming Wei; Yisen Wang 001 (NeurIPS, 2024)

Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning. - Yichuan Mo; Yuji Wang; Zeming Wei; Yisen Wang 001 (CoRR, 2024)

FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs. - Jinmin Li; Kuofeng Gao; Yang Bai; Jingyun Zhang; Shutao Xia; Yisen Wang 001 (CoRR, 2024)

How to Craft Backdoors with Unlabeled Data Alone? - Yifei Wang 001; Wenhan Ma; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2024)

A Canonization Perspective on Invariant and Equivariant Learning. - George Ma; Yifei Wang 001; Derek Lim; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2024)

EKAN: Equivariant Kolmogorov-Arnold Networks. - Lexiang Hu; Yisen Wang 001; Zhouchen Lin (CoRR, 2024)

On the Adversarial Transferability of Generalized "Skip Connections". - Yisen Wang 001; Yichuan Mo; Dongxian Wu; Mingjie Li 007; Xingjun Ma; Zhouchen Lin (CoRR, 2024)

MADE: Graph Backdoor Defense with Masked Unlearning. - Xiao Lin; Mingjie Li 007; Yisen Wang 001 (CoRR, 2024)

Sampling complex topology structures for spiking neural networks. - Shen Yan 004; Qingyan Meng; Mingqing Xiao 002; Yisen Wang 001; Zhouchen Lin (Neural Networks, 2024)

Generalization in Deep RL for TSP Problems via Equivariance and Local Search. - Wenbin Ouyang; Yisen Wang 001; Paul Weng; Shaochen Han (SN Comput. Sci., 2024)

Can In-context Learning Really Generalize to Out-of-distribution Tasks? - Qixun Wang 002; Yifei Wang 001; Xianghua Ying; Yisen Wang 001 (ICLR, 2025)

Rethinking Invariance in In-context Learning. - Lizhe Fang; Yifei Wang 001; Khashayar Gatmiry; Lei Fang; Yisen Wang 001 (ICLR, 2025)

What is Wrong with Perplexity for Long-context Language Modeling? - Lizhe Fang; Yifei Wang 001; Zhaoyang Liu 003; Chenheng Zhang; Stefanie Jegelka; Jinyang Gao; Bolin Ding; Yisen Wang 001 (ICLR, 2025)

SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation. - Mingjie Li 007; Wai Man Si; Michael Backes 001; Yang Zhang 016; Yisen Wang 001 (ICLR, 2025)

Projection Head is Secretly an Information Bottleneck. - Zhuo Ouyang; Kaiwen Hu; Qi Zhang 067; Yifei Wang 001; Yisen Wang 001 (ICLR, 2025)

Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD. - Ze Peng; Jian Zhang 018; Yisen Wang 001; Lei Qi 001; Yinghuan Shi; Yang Gao 001 (ICLR, 2025)

TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice. - Shen Yan 004; Xingyan Bin; Sijun Zhang; Yisen Wang 001; Zhouchen Lin (ICLR, 2025)

Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness. - Qi Zhang 067; Yifei Wang 001; Jingyi Cui; Xiang Pan 001; Qi Lei; Stefanie Jegelka; Yisen Wang 001 (ICLR, 2025)

AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation. - Zijun Wang; Haoqin Tu; Jieru Mei; Bingchen Zhao; Yisen Wang 001; Cihang Xie (Trans. Mach. Learn. Res., 2025)

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise. - Jingyi Cui; Yi-Ge Zhang; Hengyu Liu; Yisen Wang 001 (CoRR, 2025)

Understanding Difficult-to-learn Examples in Contrastive Learning: A Theoretical Framework for Spectral Contrastive Learning. - Yi-Ge Zhang; Jingyi Cui; Qiran Li; Yisen Wang 001 (CoRR, 2025)

When More is Less: Understanding Chain-of-Thought Length in LLMs. - Yuyang Wu; Yifei Wang 001; Tianqi Du; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2025)

Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning. - Ang Li; Yichuan Mo; Mingjie Li 007; Yifei Wang 001; Yisen Wang 001 (CoRR, 2025)

R^3-VQA: "Read the Room" by Video Social Reasoning. - Lixing Niu; Jiapeng Li; Xingping Yu; Shu Wang; Ruining Feng; Bo Wu; Ping Wei; Yisen Wang 001; Lifeng Fan (CoRR, 2025)

Advancing LLM Safe Alignment with Safety Representation Ranking. - Tianqi Du; Zeming Wei; Quan Chen; Chenheng Zhang; Yisen Wang 001 (CoRR, 2025)

Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval. - Taiye Chen; Zeming Wei; Ang Li; Yisen Wang 001 (CoRR, 2025)

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning. - Xiaojun Guo; Ang Li; Yifei Wang 001; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2025)

An Augmentation-Aware Theory for Self-Supervised Contrastive Learning. - Jingyi Cui; Hongwei Wen; Yisen Wang 001 (CoRR, 2025)

Identifying and Understanding Cross-Class Features in Adversarial Training. - Zeming Wei; Yiwen Guo; Yisen Wang 001 (CoRR, 2025)

Towards provable probabilistic safety for scalable embodied AI systems. - Linxuan He; Qing-Shan Jia; Ang Li; Hongyan Sang; Ling Wang 001; Jiwen Lu; Tao Zhang 033; Jie Zhou 001; Yi Zhang 029; Yisen Wang 001; Peng Wei; Zhongyuan Wang; et al. (CoRR, 2025)

Long-Short Alignment for Effective Long-Context Modeling in LLMs. - Tianqi Du; Haotian Huang; Yifei Wang 001; Yisen Wang 001 (CoRR, 2025)

On the Theoretical Understanding of Identifiable Sparse Autoencoders and Beyond. - Jingyi Cui; Qi Zhang 067; Yifei Wang 001; Yisen Wang 001 (CoRR, 2025)