Decoder-free Robustness Disentanglement without (Additional) Supervision. - Yifei Wang 001; Dan Peng; Furui Liu; Zhenguo Li; Zhitang Chen; Jiansheng Yang (CoRR, 2020)

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)

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

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

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-Induced Graph Implicit Nonlinear Diffusion. - Qi Chen; Yifei Wang 001; Yisen Wang 001; Jiansheng Yang; 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)

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)

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

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)

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)

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)

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)

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)

In-Context Symmetries: Self-Supervised Learning through Contextual World Models. - Sharut Gupta; Chenyu Wang 003; Yifei Wang 001; Tommi S. Jaakkola; Stefanie Jegelka (NeurIPS, 2024)

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

On the Role of Attention Masks and LayerNorm in Transformers. - Xinyi Wu 003; Amir Ajorlou; Yifei Wang 001; Stefanie Jegelka; Ali Jadbabaie (NeurIPS, 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)

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)

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

On the Emergence of Position Bias in Transformers. - Xinyi Wu 003; Yifei Wang 001; Stefanie Jegelka; Ali Jadbabaie (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)

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