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2020 – today
- 2024
[j4]Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. J. Mach. Learn. Res. 25: 24:1-24:67 (2024)- [j3]Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. J. Mach. Learn. Res. 25: 226:1-226:67 (2024) - [j2]Minshuo Chen
, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, Mengdi Wang:
Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. IEEE Trans. Inf. Theory 70(10): 7251-7272 (2024) - [c30]Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang:
Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis. AISTATS 2024: 2737-2745 - [c29]Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai:
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. ICLR 2024 - [c28]Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang:
Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling. ICML 2024 - [c27]Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei:
Theoretical insights for diffusion guidance: A case study for Gaussian mixture models. ICML 2024 - [c26]Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu:
Counterfactual Generative Models for Time-Varying Treatments. KDD 2024: 3402-3413 - [i42]Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei:
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models. CoRR abs/2403.01639 (2024) - [i41]Hengyu Fu, Zhuoran Yang, Mengdi Wang, Minshuo Chen:
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory. CoRR abs/2403.11968 (2024) - [i40]Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang:
Diffusion Model for Data-Driven Black-Box Optimization. CoRR abs/2403.13219 (2024) - [i39]Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang:
An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization. CoRR abs/2404.07771 (2024) - [i38]Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang:
Gradient Guidance for Diffusion Models: An Optimization Perspective. CoRR abs/2404.14743 (2024) - [i37]Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang:
Provable Statistical Rates for Consistency Diffusion Models. CoRR abs/2406.16213 (2024) - [i36]Hengyu Fu, Zehao Dou, Jiawei Guo, Mengdi Wang, Minshuo Chen:
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data. CoRR abs/2407.16134 (2024) - [i35]Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang:
A Theoretical Perspective for Speculative Decoding Algorithm. CoRR abs/2411.00841 (2024) - 2023
- [c25]Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. ICLR 2023 - [c24]Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao:
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. ICLR 2023 - [c23]Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. ICML 2023: 4672-4712 - [c22]Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao:
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. ICML 2023: 40911-40931 - [c21]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: 1-6 - [c20]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 - [c19]Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang:
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. NeurIPS 2023 - [i34]Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. CoRR abs/2302.07194 (2023) - [i33]Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao:
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. CoRR abs/2302.13183 (2023) - [i32]Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao:
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. CoRR abs/2303.10512 (2023) - [i31]Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu:
Counterfactual Generative Models for Time-Varying Treatments. CoRR abs/2305.15742 (2023) - [i30]Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang:
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. CoRR abs/2306.01243 (2023) - [i29]Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao:
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. CoRR abs/2306.14859 (2023) - [i28]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 abs/2307.01649 (2023) - [i27]Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai:
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. CoRR abs/2307.02884 (2023) - [i26]Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang:
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. CoRR abs/2307.07055 (2023) - [i25]Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, Mengdi Wang:
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. CoRR abs/2307.12975 (2023) - [i24]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 abs/2309.13915 (2023) - [i23]Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang:
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks. CoRR abs/2310.10556 (2023) - 2022
- [b1]Minshuo Chen:
Representation and statistical properties of deep neural networks on structured data. Georgia Institute of Technology, Atlanta, GA, USA, 2022 - [c18]Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao:
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. ICLR 2022 - [c17]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: 13669-13703 - [c16]Peiwen Tan, Lei Huang, Minshuo Chen, Yang Li, Ruiyang Ma, Jianlong Yang:
Optimal Energy Management Scheme for Wave-HESS DC Microgrid. IECON 2022: 1-6 - [c15]Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao:
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. NeurIPS 2022 - [i22]Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. CoRR abs/2201.00217 (2022) - [i21]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 abs/2201.02141 (2022) - [i20]Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie:
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks. CoRR abs/2205.02043 (2022) - [i19]Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. CoRR abs/2206.02887 (2022) - [i18]Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao:
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. CoRR abs/2206.04569 (2022) - [i17]Jiahui Cheng, Minshuo Chen, Hao Liu, Tuo Zhao, Wenjing Liao:
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization. CoRR abs/2212.00700 (2022) - 2021
- [j1]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: 9949-9957 (2021) - [c14]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: 6524-6538 - [c13]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: 543-553 - [c12]Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. ICML 2021: 6770-6780 - [c11]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: 17913-17926 - [i16]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 abs/2105.12002 (2021) - [i15]Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. CoRR abs/2109.02832 (2021) - [i14]Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao:
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. CoRR abs/2110.03677 (2021) - 2020
- [c10]Minshuo Chen, Xingguo Li, Tuo Zhao:
On Generalization Bounds of a Family of Recurrent Neural Networks. AISTATS 2020: 1233-1243 - [c9]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 - [c8]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 - [c7]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k with Optimal Transport. NeurIPS 2020 - [i13]Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao:
On Computation and Generalization of Generative Adversarial Imitation Learning. CoRR abs/2001.02792 (2020) - [i12]Minshuo Chen, Wenjing Liao, Hongyuan Zha, Tuo Zhao:
Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation. CoRR abs/2002.03938 (2020) - [i11]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k Operator with Optimal Transport. CoRR abs/2002.06504 (2020) - [i10]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR abs/2006.13436 (2020) - [i9]David Joseph Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga Sasikanth Mannem, Tuo Zhao, Hua Wang:
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers. CoRR abs/2008.10755 (2020) - [i8]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 abs/2010.05843 (2020) - [i7]Minshuo Chen, Hao Liu, Wenjing Liao, Tuo Zhao:
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks. CoRR abs/2011.01797 (2020)
2010 – 2019
- 2019
- [c6]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 - [c5]Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. DGS@ICLR 2019 - [c4]Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. ICML 2019: 6882-6892 - [c3]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: 7890-7900 - [c2]Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao:
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. NeurIPS 2019: 8172-8182 - [i6]Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. CoRR abs/1905.00158 (2019) - [i5]Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao:
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. CoRR abs/1908.01842 (2019) - [i4]Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Shortcut Connections in Residual Networks. CoRR abs/1909.04653 (2019) - [i3]Minshuo Chen, Xingguo Li, Tuo Zhao:
On Generalization Bounds of a Family of Recurrent Neural Networks. CoRR abs/1910.12947 (2019) - 2018
- [c1]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. NeurIPS 2018: 3500-3510 - [i2]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. CoRR abs/1803.02312 (2018) - [i1]Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao:
On Computation and Generalization of GANs with Spectrum Control. CoRR abs/1812.10912 (2018)
Coauthor Index
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last updated on 2024-12-12 21:55 CET by the dblp team
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