Chen, Zixiang

26 publications

ICLR 2025 Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis Zikun Zhang, Zixiang Chen, Quanquan Gu

ICML 2025 Global Convergence and Rich Feature Learning in $l$-Layer Infinite-Width Neural Networks Under $μ$ Parametrization Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu

TMLR 2025 Guided Discrete Diffusion for Electronic Health Record Generation Jun Han, Zixiang Chen, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu

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

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

ICLR 2024 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter Bartlett

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

ICML 2024 Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu

NeurIPS 2024 Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu

ICLR 2024 Understanding Transferable Representation Learning and Zero-Shot Transfer in CLIP Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu

ICLR 2023 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael Jordan

ICML 2023 Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu

ICML 2023 Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

ICLR 2023 How Does Semi-Supervised Learning with Pseudo-Labelers Work? a Case Study Yiwen Kou, Zixiang Chen, Yuan Cao, 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

ICLR 2023 Understanding Train-Validation Split in Meta-Learning with Neural Networks Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu

NeurIPSW 2023 Understanding Transferable Representation Learning and Zero-Shot Transfer in CLIP Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu

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

AISTATS 2022 Self-Training Converts Weak Learners to Strong Learners in Mixture Models Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu

NeurIPSW 2022 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael Jordan

ALT 2022 Almost Optimal Algorithms for Two-Player Zero-Sum Linear Mixture Markov Games Zixiang Chen, Dongruo Zhou, Quanquan Gu

NeurIPS 2022 Benign Overfitting in Two-Layer Convolutional Neural Networks Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu

ALT 2022 Faster Perturbed Stochastic Gradient Methods for Finding Local Minima Zixiang Chen, Dongruo Zhou, Quanquan Gu

NeurIPS 2022 Towards Understanding the Mixture-of-Experts Layer in Deep Learning Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li

ICLR 2021 How Much Over-Parameterization Is Sufficient to Learn Deep ReLU Networks? Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu

NeurIPS 2020 A Generalized Neural Tangent Kernel Analysis for Two-Layer Neural Networks Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang