Publications
Preprints
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
Hengyu Fu, Zehao Dou, Jiawei Guo, Mengdi Wang, and Minshuo Chen
Gradient Guidance for Diffusion Models: An Optimization Perspective
Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, and Mengdi Wang
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory
Hengyu Fu, Zhuoran Yang, Mengdi Wang, and Minshuo Chen
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, and Mengdi Wang
Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks
Minshuo Chen, Wenjing Liao, Hongyuan Zha, and Tuo Zhao
Journal Papers
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
Minshuo Chen, Haoming Jiang, Wenjing Liao, and Tuo Zhao
IMA, Information and Inference, 2022
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie
IMA, Information and Inference, 2023
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization
Jiahui Cheng, Minshuo Chen, Hao Liu, Tuo Zhao, Wenjing Liao
Sampling Theory, Signal Processing, and Data Analysis, 2023
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
Minshuo Chen, Hao Liu, Wenjing Liao, and Tuo Zhao
Major revision at Mathematics of Operations Research
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, and Wenjing Liao
Journal of Machine Learning Research, 2024
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, and Mengdi Wang
IEEE Transactions on Information Theory, 2024
Challenges and Opportunities of Diffusion Models for Generative AI: Applications, Guided Generation, Statistical Rates and Optimization
Minshuo Chen, Song Mei, Jianqing Fan, and Mengdi Wang
National Science Review, to appear
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, and Tuo Zhao
Journal of Machine Learning Research, 2024
Conference Papers
Provable Statistical Rates for Consistency Diffusion Models
Zehao Dou, Minshuo Chen, Mengdi Wang, and Zhuoran Yang
International Conference on Machine Learning (ICML), 2024
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, and Yuting Wei
International Conference on Machine Learning (ICML), 2024
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, and Yu Bai
International Conference on Learning Representations (ICLR), 2024
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang
Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, and Mengdi Wang
Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Minshuo Chen*, Kaixuan Huang*, Tuo Zhao, and Mengdi Wang (* Equal Contribution)
International Conference on Machine Learning (ICML), 2023
Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, and Tuo Zhao
International Conference on Machine Learning (ICML), 2023
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao
International Conference on Learning Representations (ICLR), 2023
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
Xiang Ji, Minshuo Chen, Mengdi Wang, and Tuo Zhao
International Conference on Learning Representations (ICLR), 2023
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Biraj Dahal, Alex Havrilla, Minshuo Chen, Tuo Zhao, and Wenjing Liao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2022
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, and Tuo Zhao
International Conference on Machine Learning (ICML), 2022
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Yuqing Wang, Minshuo Chen, Tuo Zhao, and Molei Tao
International Conference on Learning Representations (ICLR), 2022
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2021
How Important is the Train-Validation Split in Meta-Learning
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang, and Caiming Xiong
International Conference on Machine Learning (ICML), 2021
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Hao Liu, Minshuo Chen, Tuo Zhao, and Wenjing Liao
International Conference on Machine Learning (ICML), 2021
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, and Richard Socher
Annual Conference on Neural Information Processing Systems (NeurIPS), 2020
Differentiable Top-k Operator with Optimal Transport
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
Annual Conference on Neural Information Processing Systems (NeurIPS), 2020
On Generalization Bounds of a Family of Recurrent Neural Networks
Minshuo Chen, Xingguo Li, and Tuo Zhao
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
On Computation and Generalization of Generative Adversarial Imitation Learning
Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, and Tuo Zhao
International Conference on Learning Representations (ICLR), 2020
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
Minshuo Chen, Haoming Jiang, Wenjing Liao, and Tuo Zhao (Alphabetical Order)
Annual Conference on Neural Information Processing Systems (NeurIPS), 2019
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Tianyi Liu*, Minshuo Chen*, Mo Zhou, Simon Du, Enlu Zhou, and Tuo Zhao (* Equal Contribution)
Annual Conference on Neural Information Processing Systems (NeurIPS), 2019
On Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, and Hongyuan Zha
International Conference on Machine Learning (ICML), 2019
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, and Tuo Zhao
International Conference on Learning Representations (ICLR), 2019
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Minshuo Chen, Lin Yang, Mengdi Wang, and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2018
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