Minshuo Chen

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 70

Top Topics

Sample Complexity

Low Dimensional Manifolds

Diffusion Models

Nonparametric Estimation

Top Venues

Publications

Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models. CoRR (2024)
Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang
Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis. AISTATS (2024)
Hengyu Fu, Zehao Dou, Jiawei Guo, Mengdi Wang, Minshuo Chen
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data. CoRR (2024)
Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang
Provable Statistical Rates for Consistency Diffusion Models. CoRR (2024)
Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. ICLR (2024)
Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang
Diffusion Model for Data-Driven Black-Box Optimization. CoRR (2024)
Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang
An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization. CoRR (2024)
Hengyu Fu, Zhuoran Yang, Mengdi Wang, Minshuo Chen
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory. CoRR (2024)
Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang
Gradient Guidance for Diffusion Models: An Optimization Perspective. CoRR (2024)
Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu
Counterfactual Generative Models for Time-Varying Treatments. KDD (2024)
Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. CoRR (2023)
Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. ICML (2023)
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. ICLR (2023)
Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, Mengdi Wang
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. CoRR (2023)
Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. CoRR (2023)
Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. ICML (2023)
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. NeurIPS (2023)
Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. CoRR (2023)
Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu
Counterfactual Generative Models for Time-Varying Treatments. CoRR (2023)
Minshuo Chen, Lei Huang, Yuan Li, Jiyu Zhang, Peiwen Tan, Ghulam Ahmad
Design and Analysis of a Field Modulated Transverse Flux Linear Generator Used in Direct Drive Wave Energy Converter. IECON (2023)
Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. CoRR (2023)
Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. ICLR (2023)
Biraj Dahal, Alex Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. CoRR (2023)
Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks. CoRR (2023)
Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. CoRR (2023)
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. CoRR (2023)
Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. CoRR (2023)
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. CoRR (2023)
Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. NeurIPS (2023)
Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. ICLR (2022)
Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. CoRR (2022)
Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. CoRR (2022)
Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks. CoRR (2022)
Siawpeng Er, Edward Liu, Minshuo Chen, Yan Li, Yuqi Liu, Tuo Zhao, Hua Wang
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network. CoRR (2022)
Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. ICML (2022)
Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. NeurIPS (2022)
Peiwen Tan, Lei Huang, Minshuo Chen, Yang Li, Ruiyang Ma, Jianlong Yang
Optimal Energy Management Scheme for Wave-HESS DC Microgrid. IECON (2022)
Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. CoRR (2022)
Jiahui Cheng, Minshuo Chen, Hao Liu, Tuo Zhao, Wenjing Liao
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization. CoRR (2022)
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
How Important is the Train-Validation Split in Meta-Learning? ICML (2021)
Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. CoRR (2021)
Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. ICML (2021)
Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. ACL/IJCNLP (1) (2021)
Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. CoRR (2021)
Minshuo Chen, Lei Huang, Peiwen Tan, Yang Li, Ghulam Ahmad, Minqiang Hu
A Stator-PM Transverse Flux Permanent Magnet Linear Generator for Direct Drive Wave Energy Converter. IEEE Access 9 (2021)
Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. CoRR (2021)
Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. NeurIPS (2021)
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
How Important is the Train-Validation Split in Meta-Learning? CoRR (2020)
David Joseph Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga S. Mannem, Tuo Zhao, Hua Wang
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers. CoRR (2020)
Minshuo Chen, Hao Liu, Wenjing Liao, Tuo Zhao
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks. CoRR (2020)
Minshuo Chen, Xingguo Li, Tuo Zhao
On Generalization Bounds of a Family of Recurrent Neural Networks. AISTATS (2020)
Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao
On Computation and Generalization of Generative Adversarial Imitation Learning. CoRR (2020)
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. NeurIPS (2020)
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
Differentiable Top-k Operator with Optimal Transport. CoRR (2020)
Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao
On Computation and Generalization of Generative Adversarial Imitation Learning. ICLR (2020)
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR (2020)
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
Differentiable Top-k with Optimal Transport. NeurIPS (2020)
Minshuo Chen, Wenjing Liao, Hongyuan Zha, Tuo Zhao
Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation. CoRR (2020)
Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
On Scalable and Efficient Computation of Large Scale Optimal Transport. DGS@ICLR (2019)
Minshuo Chen, Xingguo Li, Tuo Zhao
On Generalization Bounds of a Family of Recurrent Neural Networks. CoRR (2019)
Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
On Scalable and Efficient Computation of Large Scale Optimal Transport. ICML (2019)
Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control. ICLR (Poster) (2019)
Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. NeurIPS (2019)
Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
Towards Understanding the Importance of Shortcut Connections in Residual Networks. NeurIPS (2019)
Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
On Scalable and Efficient Computation of Large Scale Optimal Transport. CoRR (2019)
Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. CoRR (2019)
Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
Towards Understanding the Importance of Shortcut Connections in Residual Networks. CoRR (2019)
Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao
On Computation and Generalization of GANs with Spectrum Control. CoRR (2018)
Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. NeurIPS (2018)
Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. CoRR (2018)