Minshuo Chen - RankMe

89 papers A* 28A 2C 2Journal 53Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity. CoRR Zixuan Zhang, Kaixuan Huang, Tuo Zhao, Mengdi Wang, Minshuo Chen
2026	J	`jnl`	Training-Free Adaptation of Diffusion Models via Doob's CoRR Qijie Zhu, Zeqi Ye, Han Liu, Zhaoran Wang, Minshuo Chen
2025	J	`jnl`	A Tubular Linear-Rotary Generator With Dual Stators for Direct Drive Wind-Wave Complementary Energy Generation Platform. IEEE Access Jing Zhang, Minshuo Chen, Yulei Liu
2025	J	`jnl`	Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure. CoRR Minshuo Chen, Renyuan Xu, Yumin Xu, Ruixun Zhang
2025	A*	`conf`	Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data. ICLR Hengyu Fu, Zehao Dou, Jiawei Guo, Mengdi Wang, Minshuo Chen
2025	J	`jnl`	Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification. CoRR Zeqi Ye, Minshuo Chen
2025	A*	`conf`	On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality. ICLR Jerry Yao-Chieh Hu, Weimin Wu, Yi-Chen Lee, Yu-Chao Huang, Minshuo Chen, Han Liu
2025	J	`jnl`	Provable Separations between Memorization and Generalization in Diffusion Models. CoRR Zeqi Ye, Qijie Zhu, Molei Tao, Minshuo Chen
2024	A*	`conf`	A Theoretical Perspective for Speculative Decoding Algorithm. NeurIPS Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang
2024	J	`jnl`	A Theoretical Perspective for Speculative Decoding Algorithm. CoRR Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang
2024	J	`jnl`	An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization. CoRR Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang
2024	A*	`conf`	Counterfactual Generative Models for Time-Varying Treatments. KDD Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu
2024	J	`jnl`	Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. J. Mach. Learn. Res. Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao
2024	J	`jnl`	Diffusion Model for Data-Driven Black-Box Optimization. CoRR Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang
2024	J	`jnl`	Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data. CoRR Hengyu Fu, Zehao Dou, Jiawei Guo, Mengdi Wang, Minshuo Chen
2024	J	`jnl`	Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. IEEE Trans. Inf. Theory Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, Mengdi Wang
2024	A*	`conf`	Gradient Guidance for Diffusion Models: An Optimization Perspective. NeurIPS Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang
2024	J	`jnl`	Gradient Guidance for Diffusion Models: An Optimization Perspective. CoRR Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang
2024	A*	`conf`	Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. NeurIPS Zixuan Zhang, Kaiqi Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang
2024	J	`jnl`	On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality. CoRR Jerry Yao-Chieh Hu, Weimin Wu, Yi-Chen Lee, Yu-Chao Huang, Minshuo Chen, Han Liu
2024	A	`conf`	Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis. AISTATS Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang
2024	J	`jnl`	Provable Statistical Rates for Consistency Diffusion Models. CoRR Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang
2024	J	`jnl`	Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. J. Mach. Learn. Res. Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
2024	A*	`conf`	Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. ICLR Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai
2024	J	`jnl`	Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models. CoRR Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei
2024	A*	`conf`	Theoretical insights for diffusion guidance: A case study for Gaussian mixture models. ICML Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei
2024	A*	`conf`	Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling. ICML Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang
2024	J	`jnl`	Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory. CoRR Hengyu Fu, Zhuoran Yang, Mengdi Wang, Minshuo Chen
2023	A*	`conf`	Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. ICLR Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao
2023	J	`jnl`	Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. CoRR Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao
2023	J	`jnl`	Counterfactual Generative Models for Time-Varying Treatments. CoRR Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu
2023	C	`conf`	Design and Analysis of a Field Modulated Transverse Flux Linear Generator Used in Direct Drive Wave Energy Converter. IECON Minshuo Chen, Lei Huang, Yuan Li, Jiyu Zhang, Peiwen Tan, Ghulam Ahmad
2023	A*	`conf`	Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. ICML Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao
2023	J	`jnl`	Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. CoRR Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao
2023	A*	`conf`	Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. NeurIPS Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
2023	J	`jnl`	Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. CoRR Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
2023	J	`jnl`	Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. CoRR Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang
2023	J	`jnl`	On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. CoRR Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao
2023	J	`jnl`	Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. CoRR Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, Mengdi Wang
2023	A*	`conf`	Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. NeurIPS Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
2023	J	`jnl`	Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. CoRR Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
2023	J	`jnl`	Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. CoRR Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
2023	A*	`conf`	Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. ICLR Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
2023	J	`jnl`	Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks. CoRR Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang
2023	J	`jnl`	Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. CoRR Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai
2023	A*	`conf`	Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. ICML Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang
2023	J	`jnl`	Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. CoRR Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang
2022	J	`jnl`	A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks. CoRR Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie
2022	A*	`conf`	Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. ICML Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao
2022	J	`jnl`	Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. CoRR Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao
2022	J	`jnl`	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 Siawpeng Er, Edward Liu, Minshuo Chen, Yan Li, Yuqi Liu, Tuo Zhao, Hua Wang
2022	J	`jnl`	Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. CoRR Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao
2022	J	`jnl`	High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization. CoRR Jiahui Cheng, Minshuo Chen, Hao Liu, Tuo Zhao, Wenjing Liao
2022	A*	`conf`	Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. ICLR Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao
2022	A*	`conf`	On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. NeurIPS Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao
2022	C	`conf`	Optimal Energy Management Scheme for Wave-HESS DC Microgrid. IECON Peiwen Tan, Lei Huang, Minshuo Chen, Yang Li, Ruiyang Ma, Jianlong Yang
2022	—	`—`	Representation and statistical properties of deep neural networks on structured data. Minshuo Chen
2022	J	`jnl`	Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. CoRR Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
2021	J	`jnl`	A Stator-PM Transverse Flux Permanent Magnet Linear Generator for Direct Drive Wave Energy Converter. IEEE Access Minshuo Chen, Lei Huang, Peiwen Tan, Yang Li, Ghulam Ahmad, Minqiang Hu
2021	A*	`conf`	Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. ICML Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao
2021	J	`jnl`	Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. CoRR Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao
2021	A*	`conf`	How Important is the Train-Validation Split in Meta-Learning? ICML Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
2021	J	`jnl`	Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. CoRR Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao
2021	A*	`conf`	Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. NeurIPS Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao
2021	—	`conf`	Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. ACL/IJCNLP (1) Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
2021	J	`jnl`	Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. CoRR Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
2020	J	`jnl`	Differentiable Top-k Operator with Optimal Transport. CoRR Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
2020	A*	`conf`	Differentiable Top-k with Optimal Transport. NeurIPS Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
2020	J	`jnl`	Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks. CoRR Minshuo Chen, Hao Liu, Wenjing Liao, Tuo Zhao
2020	J	`jnl`	How Important is the Train-Validation Split in Meta-Learning? CoRR Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
2020	A*	`conf`	On Computation and Generalization of Generative Adversarial Imitation Learning. ICLR Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao
2020	J	`jnl`	On Computation and Generalization of Generative Adversarial Imitation Learning. CoRR Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao
2020	A	`conf`	On Generalization Bounds of a Family of Recurrent Neural Networks. AISTATS Minshuo Chen, Xingguo Li, Tuo Zhao
2020	J	`jnl`	Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers. CoRR David Joseph Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga Sasikanth Mannem, Tuo Zhao, Hua Wang
2020	J	`jnl`	Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation. CoRR Minshuo Chen, Wenjing Liao, Hongyuan Zha, Tuo Zhao
2020	A*	`conf`	Towards Understanding Hierarchical Learning: Benefits of Neural Representations. NeurIPS Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
2020	J	`jnl`	Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
2019	A*	`conf`	Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. NeurIPS Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao
2019	J	`jnl`	Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. CoRR Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao
2019	—	`conf`	On Computation and Generalization of Generative Adversarial Networks under Spectrum Control. ICLR (Poster) Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao
2019	J	`jnl`	On Generalization Bounds of a Family of Recurrent Neural Networks. CoRR Minshuo Chen, Xingguo Li, Tuo Zhao
2019	—	`conf`	On Scalable and Efficient Computation of Large Scale Optimal Transport. DGS@ICLR Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
2019	A*	`conf`	On Scalable and Efficient Computation of Large Scale Optimal Transport. ICML Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
2019	J	`jnl`	On Scalable and Efficient Computation of Large Scale Optimal Transport. CoRR Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
2019	A*	`conf`	Towards Understanding the Importance of Shortcut Connections in Residual Networks. NeurIPS Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
2019	J	`jnl`	Towards Understanding the Importance of Shortcut Connections in Residual Networks. CoRR Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
2018	A*	`conf`	Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. NeurIPS Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
2018	J	`jnl`	Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. CoRR Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
2018	J	`jnl`	On Computation and Generalization of GANs with Spectrum Control. CoRR Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao