On the equivalences and differences of evolutionary algorithms. - Haiping Ma; Dan Simon; Minrui Fei; Zixiang Chen (Eng. Appl. Artif. Intell., 2013)

Hybrid biogeography-based evolutionary algorithms. - Haiping Ma; Dan Simon; Minrui Fei; Xinzhan Shu; Zixiang Chen (Eng. Appl. Artif. Intell., 2014)

k-CoFi: Modeling k-Granularity Preference Context in Collaborative Filtering. - Yunfeng Huang; Zixiang Chen; Lin Li 039; Weike Pan; Zhiguang Shan; Zhong Ming 001 (SmartCom, 2017)

Stein Neural Sampler. - Tianyang Hu; Zixiang Chen; Hanxi Sun; Jincheng Bai; Mao Ye; Guang Cheng (CoRR, 2018)

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (NeurIPS, 2020)

Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (CoRR, 2020)

k-Reciprocal nearest neighbors algorithm for one-class collaborative filtering. - Wei Cai; Weike Pan; Jixiong Liu; Zixiang Chen; Zhong Ming 001 (Neurocomputing, 2020)

Conditional restricted Boltzmann machine for item recommendation. - Zixiang Chen; Wanqi Ma; Wei Dai; Weike Pan; Zhong Ming 001 (Neurocomputing, 2020)

Matrix factorization with heterogeneous multiclass preference context. - Jing Lin 008; Weike Pan; Lin Li 039; Zixiang Chen; Zhong Ming 001 (Neurocomputing, 2020)

CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging. - Zhiyuan Huang; Zixiang Chen; Qiyang Zhang; Guotao Quan; Min Ji; Chengjin Zhang; Yongfeng Yang; Xin Liu 053; Dong Liang 001; Hairong Zheng; Zhanli Hu (IEEE Trans. Computational Imaging, 2020)

How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? - Zixiang Chen; Yuan Cao 006; Difan Zou; Quanquan Gu (ICLR, 2021)

Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Dynamic PET Imaging Using Dual Texture Features. - Zhanglei Ouyang; Shujun Zhao; Zhaoping Cheng; Yanhua Duan; Zixiang Chen; Na Zhang 001; Dong Liang 001; Zhanli Hu (Frontiers Comput. Neurosci., 2021)

Factored heterogeneous similarity model for recommendation with implicit feedback. - Yongxin Ni; Xiancong Chen; Weike Pan; Zixiang Chen; Zhong Ming 001 (Neurocomputing, 2021)

Learning a Deep CNN Denoising Approach Using Anatomical Prior Information Implemented With Attention Mechanism for Low-Dose CT Imaging on Clinical Patient Data From Multiple Anatomical Sites. - Zhenxing Huang; Xinfeng Liu; Rongpin Wang; Zixiang Chen; Yongfeng Yang; Xin Liu 053; Hairong Zheng; Dong Liang 001; Zhanli Hu (IEEE J. Biomed. Health Informatics, 2021)

Self-training Converts Weak Learners to Strong Learners in Mixture Models. - Spencer Frei; Difan Zou; Zixiang Chen; Quanquan Gu (AISTATS, 2022)

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (ALT, 2022)

Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (ALT, 2022)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Misha Belkin; Quanquan Gu (NeurIPS, 2022)

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

TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner. - Zhengyong Huang; Si Tang; Zixiang Chen; Guoshuai Wang; Hao Shen; Yun Zhou; Haining Wang; Wei Fan; Dong Liang 001; Yingying Hu; Zhanli Hu (Comput. Biol. Medicine, 2022)

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation. - Zhengyong Huang; Sijuan Zou; Guoshuai Wang; Zixiang Chen; Hao Shen; Haiyan Wang; Na Zhang 001; Lu Zhang; Fan Yang 049; Haining Wang; Dong Liang 001; Tianye Niu; et al. (Comput. Methods Programs Biomed., 2022)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Mikhail Belkin; Quanquan Gu (CoRR, 2022)

Towards Understanding Mixture of Experts in Deep Learning. - Zixiang Chen; Yihe Deng; Yue Wu; Quanquan Gu; Yuanzhi Li (CoRR, 2022)

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Zixiang Chen; Chris Junchi Li; Angela Yuan; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

High Temporal Resolution Total-Body Dynamic PET Imaging Based on Pixel-Level Time-Activity Curve Correction. - Zixiang Chen; Yaping Wu; Na Zhang 001; Tao Sun 025; Yu Shen; Hairong Zheng; Dong Liang 001; Meiyun Wang; Zhanli Hu (IEEE Trans. Biomed. Eng., 2022)

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

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

Understanding Train-Validation Split in Meta-Learning with Neural Networks. - Xinzhe Zuo; Zixiang Chen; Huaxiu Yao; 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)

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

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (CoRR, 2023)

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

Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves. - Yihe Deng; Weitong Zhang; Zixiang 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)

Low-dose dynamic cerebral perfusion CT reconstruction based on voxel-level TAC correction (VTC). - Zixiang Chen; Na Zhang 001; Qiyang Zhang; Kaiyi Liang; Zhenxing Huang; Guotao Quan; Xiang Li; Dong Liang 001; Zhanli Hu (Biomed. Signal Process. Control., 2023)

Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. - Zixiang Chen; Yihe Deng; Yuanzhi Li; 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 L. Bartlett (ICLR, 2024)

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

Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation. - Huizhuo Yuan; Zixiang Chen; Kaixuan Ji; Quanquan Gu (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)

Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study. - Zihan Li; Yibo Zhang; Zixiang Chen; Jiangming Chen; Hui Hou; Cheng Wang; Zheng Lu; Xiaoming Wang; Xiaoping Geng; Fubao Liu (Frontiers Digit. Health, 2024)

LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images. - Qiang Fang; Yue Yang; Hao Wang; Hanxi Sun; Jiangming Chen; Zixiang Chen; Tian Pu; Xiaoqing Zhang; Fubao Liu (Health Inf. Sci. Syst., 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)

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

Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μP Parametrization. - Zixiang Chen; Greg Yang; Qingyue Zhao; Quanquan Gu (CoRR, 2025)