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

How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. - Yiwen Kou; Zixiang Chen; Yuan Cao 006; Quanquan Gu (ICLR, 2023)

Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. - Yiwen Kou; Zixiang Chen; Yuanzhou Chen; Quanquan Gu (ICML, 2023)

Why Does Sharpness-Aware Minimization Generalize Better Than SGD? - Zixiang Chen; Junkai Zhang; Yiwen Kou; Xiangning Chen; Cho-Jui Hsieh; Quanquan Gu (NeurIPS, 2023)

Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data. - Yiwen Kou; Zixiang Chen; Quanquan Gu (NeurIPS, 2023)

Benign Overfitting for Two-layer ReLU Networks. - Yiwen Kou; Zixiang Chen; Yuanzhou Chen; Quanquan Gu (CoRR, 2023)

Fast Sampling via De-randomization for Discrete Diffusion Models. - Zixiang Chen; Huizhuo Yuan; Yongqian Li; Yiwen Kou; Junkai Zhang; Quanquan Gu (CoRR, 2023)

Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time. - Zixiang Chen; Huizhuo Yuan; Yongqian Li; Yiwen Kou; Junkai Zhang; Quanquan Gu (NeurIPS, 2024)

GraphVis: Boosting LLMs with Visual Knowledge Graph Integration. - Yihe Deng; Chenchen Ye 001; Zijie Huang 002; Mingyu Derek Ma; Yiwen Kou; Wei Wang 010 (NeurIPS, 2024)

Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent. - Yiwen Kou; Zixiang Chen; Quanquan Gu; Sham M. Kakade (NeurIPS, 2024)

Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent. - Yiwen Kou; Zixiang Chen; Quanquan Gu; Sham M. Kakade (CoRR, 2024)

On the Power of Multitask Representation Learning with Gradient Descent. - Qiaobo Li; Zixiang Chen; Yihe Deng; Yiwen Kou; Yuan Cao 006; Quanquan Gu (AISTATS, 2025)

Guided Discrete Diffusion for Electronic Health Record Generation. - Jun Han; Zixiang Chen; Yongqian Li; Yiwen Kou; Eran Halperin; Robert E. Tillman; Quanquan Gu (Trans. Mach. Learn. Res., 2025)