Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. - Minshuo Chen; Lin Yang 011; Mengdi Wang; Tuo Zhao (NeurIPS, 2018)

On Computation and Generalization of GANs with Spectrum Control. - Haoming Jiang; Zhehui Chen; Minshuo Chen; Feng Liu; Dingding Wang 001; Tuo Zhao (CoRR, 2018)

On Computation and Generalization of Generative Adversarial Networks under Spectrum Control. - Haoming Jiang; Zhehui Chen; Minshuo Chen; Feng Liu; Dingding Wang 001; Tuo Zhao (ICLR (Poster), 2019)

On Scalable and Efficient Computation of Large Scale Optimal Transport. - Yujia Xie; Minshuo Chen; Haoming Jiang; Tuo Zhao; Hongyuan Zha (ICML, 2019)

Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. - Minshuo Chen; Haoming Jiang; Wenjing Liao; Tuo Zhao (NeurIPS, 2019)

Towards Understanding the Importance of Shortcut Connections in Residual Networks. - Tianyi Liu; Minshuo Chen; Mo Zhou; Simon S. Du; Enlu Zhou; Tuo Zhao (NeurIPS, 2019)

On Generalization Bounds of a Family of Recurrent Neural Networks. - Minshuo Chen; Xingguo Li; Tuo Zhao (AISTATS, 2020)

On Computation and Generalization of Generative Adversarial Imitation Learning. - Minshuo Chen; Yizhou Wang 006; Tianyi Liu; Zhuoran Yang; Xingguo Li; Zhaoran Wang 001; Tuo Zhao (ICLR, 2020)

Towards Understanding Hierarchical Learning: Benefits of Neural Representations. - Minshuo Chen; Yu Bai 017; Jason D. Lee; Tuo Zhao; Huan Wang 014; Caiming Xiong; Richard Socher (NeurIPS, 2020)

Differentiable Top-k with Optimal Transport. - Yujia Xie; Hanjun Dai; Minshuo Chen; Bo Dai 001; Tuo Zhao; Hongyuan Zha; Wei Wei 019; Tomas Pfister (NeurIPS, 2020)

Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation. - Minshuo Chen; Wenjing Liao; Hongyuan Zha; Tuo Zhao (CoRR, 2020)

Differentiable Top-k Operator with Optimal Transport. - Yujia Xie; Hanjun Dai; Minshuo Chen; Bo Dai 001; Tuo Zhao; Hongyuan Zha; Wei Wei 019; Tomas Pfister (CoRR, 2020)

Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers. - David Joseph Munzer; Siawpeng Er; Minshuo Chen; Yan Li 074; Naga Sasikanth Mannem; Tuo Zhao; Hua Wang 006 (CoRR, 2020)

Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks. - Minshuo Chen; Hao Liu 028; Wenjing Liao; Tuo Zhao (CoRR, 2020)

Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. - Chen Liang 006; Simiao Zuo; Minshuo Chen; Haoming Jiang; Xiaodong Liu 003; Pengcheng He; Tuo Zhao; Weizhu Chen (ACL/IJCNLP (1), 2021)

How Important is the Train-Validation Split in Meta-Learning? - Yu Bai 017; Minshuo Chen; Pan Zhou 002; Tuo Zhao; Jason D. Lee; Sham M. Kakade; Huan Wang 014; Caiming Xiong (ICML, 2021)

Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. - Hao Liu 028; Minshuo Chen; Tuo Zhao; Wenjing Liao (ICML, 2021)

Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. - Minshuo Chen; Yan Li 074; Ethan Wang; Zhuoran Yang; Zhaoran Wang 001; Tuo Zhao (NeurIPS, 2021)

A Stator-PM Transverse Flux Permanent Magnet Linear Generator for Direct Drive Wave Energy Converter. - Minshuo Chen; Lei Huang 013; Peiwen Tan; Yang Li; Ghulam Ahmad; Minqiang Hu (IEEE Access, 2021)

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. - Yuqing Wang; Minshuo Chen; Tuo Zhao; Molei Tao (ICLR, 2022)

Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. - Hao Liu 028; Minshuo Chen; Siawpeng Er; Wenjing Liao; Tong Zhang 001; Tuo Zhao (ICML, 2022)

Optimal Energy Management Scheme for Wave-HESS DC Microgrid. - Peiwen Tan; Lei Huang 013; Minshuo Chen; Yang Li; Ruiyang Ma; Jianlong Yang (IECON, 2022)

On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. - Biraj Dahal; Alexander Havrilla; Minshuo Chen; Tuo Zhao; Wenjing Liao (NeurIPS, 2022)

Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network. - Siawpeng Er; Edward Liu; Minshuo Chen; Yan Li 074; Yuqi Liu; Tuo Zhao; Hua Wang 006 (CoRR, 2022)

A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks. - Jie Wang 049; Minshuo Chen; Tuo Zhao; Wenjing Liao; Yao Xie 002 (CoRR, 2022)

High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization. - Jiahui Cheng; Minshuo Chen; Hao Liu 028; Tuo Zhao; Wenjing Liao (CoRR, 2022)

Representation and statistical properties of deep neural networks on structured data. - Minshuo Chen (2022)

Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. - Xiang Ji; Minshuo Chen; Mengdi Wang; Tuo Zhao (ICLR, 2023)

Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. - Qingru Zhang; Minshuo Chen; Alexander Bukharin; Pengcheng He; Yu Cheng 001; Weizhu Chen; Tuo Zhao (ICLR, 2023)

Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. - Minshuo Chen; Kaixuan Huang; Tuo Zhao; Mengdi Wang (ICML, 2023)

Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. - Zixuan Zhang; Minshuo Chen; Mengdi Wang; Wenjing Liao; Tuo Zhao (ICML, 2023)

Design and Analysis of a Field Modulated Transverse Flux Linear Generator Used in Direct Drive Wave Energy Converter. - Minshuo Chen; Lei Huang 013; Yuan Li; Jiyu Zhang; Peiwen Tan; Ghulam Ahmad (IECON, 2023)

Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. - Minshuo Chen; Yu Bai 017; H. Vincent Poor; Mengdi Wang (NeurIPS, 2023)

Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. - Hui Yuan 002; Kaixuan Huang; Chengzhuo Ni; Minshuo Chen; Mengdi Wang (NeurIPS, 2023)

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. - Kaiqi Zhang 002; Zixuan Zhang; Minshuo Chen; Mengdi Wang; Tuo Zhao; Yu-Xiang Wang 003 (CoRR, 2023)

Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. - Xiang Ji; Huazheng Wang; Minshuo Chen; Tuo Zhao; Mengdi Wang (CoRR, 2023)

Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks. - Zihao Li; Xiang Ji; Minshuo Chen; Mengdi Wang (CoRR, 2023)

Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis. - Zihao Li; Xiang Ji; Minshuo Chen; Mengdi Wang (AISTATS, 2024)

Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. - Jiacheng Guo; Minshuo Chen; Huan Wang 014; Caiming Xiong; Mengdi Wang; Yu Bai 017 (ICLR, 2024)

Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling. - Zehao Dou; Minshuo Chen; Mengdi Wang; Zhuoran Yang (ICML, 2024)

Theoretical insights for diffusion guidance: A case study for Gaussian mixture models. - Yuchen Wu; Minshuo Chen; Zihao Li; Mengdi Wang; Yuting Wei 001 (ICML, 2024)

Counterfactual Generative Models for Time-Varying Treatments. - Shenghao Wu; Wenbin Zhou; Minshuo Chen; Shixiang Zhu (KDD, 2024)

A Theoretical Perspective for Speculative Decoding Algorithm. - Ming Yin 003; Minshuo Chen; Kaixuan Huang; Mengdi Wang (NeurIPS, 2024)

Gradient Guidance for Diffusion Models: An Optimization Perspective. - Yingqing Guo; Hui Yuan 002; Yukang Yang; Minshuo Chen; Mengdi Wang (NeurIPS, 2024)

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. - Zixuan Zhang; Kaiqi Zhang 002; Minshuo Chen; Yuma Takeda; Mengdi Wang; Tuo Zhao; Yu-Xiang Wang 003 (NeurIPS, 2024)

Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory. - Hengyu Fu; Zhuoran Yang; Mengdi Wang; Minshuo Chen (CoRR, 2024)

Diffusion Model for Data-Driven Black-Box Optimization. - Zihao Li; Hui Yuan 002; Kaixuan Huang; Chengzhuo Ni; Yinyu Ye 001; Minshuo Chen; Mengdi Wang (CoRR, 2024)

An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization. - Minshuo Chen; Song Mei; Jianqing Fan; Mengdi Wang (CoRR, 2024)

Provable Statistical Rates for Consistency Diffusion Models. - Zehao Dou; Minshuo Chen; Mengdi Wang; Zhuoran Yang (CoRR, 2024)

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. - Hao Liu 028; Haizhao Yang; Minshuo Chen; Tuo Zhao; Wenjing Liao (J. Mach. Learn. Res., 2024)

Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. - Zhenghao Xu; Xiang Ji; Minshuo Chen; Mengdi Wang; Tuo Zhao (J. Mach. Learn. Res., 2024)

Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. - Minshuo Chen; Jie Meng; Yu Bai 017; Yinyu Ye 001; H. Vincent Poor; Mengdi Wang (IEEE Trans. Inf. Theory, 2024)

Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data. - Hengyu Fu; Zehao Dou; Jiawei Guo; Mengdi Wang; Minshuo Chen (ICLR, 2025)

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality. - Jerry Yao-Chieh Hu; Weimin Wu; Yi-Chen Lee; Yu-Chao Huang; Minshuo Chen; Han Liu 001 (ICLR, 2025)

A Tubular Linear-Rotary Generator With Dual Stators for Direct Drive Wind-Wave Complementary Energy Generation Platform. - Jing Zhang; Minshuo Chen; Yulei Liu (IEEE Access, 2025)

Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure. - Minshuo Chen; Renyuan Xu; Yumin Xu; Ruixun Zhang (CoRR, 2025)