← Authors

Minshuo Chen

34 papers · 2018–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (7) 🐝 Cross-Pollinator (5) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (39) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (7) 🀝 Dynamic Duo (24) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (123) πŸ“ˆ Trend Setter ⚑ Prolific Year (6) ❓ The Questioner πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (34)

Conferences

NIPS (12) ICLR (8) ICML (8) AISTATS (2) JMLR (2) ACL (1) IJCNLP (1)

Top co-authors

Tuo Zhao (24) Mengdi Wang (15) Wenjing Liao (6) Haoming Jiang (5) Yu Bai (4) Caiming Xiong (3) Kaixuan Huang (3) Xiang Ji (3) Weizhu Chen (3) Hao Liu (3)

Research topics

Optimization & Theory (1)

Keywords

sample complexity (7) function approximation (5) curse of dimensionality (3) deep neural network (3) reinforcement learning (3) convolutional residual network (3) manifold learning (3) neural network approximation (2) approximation theory (2) distribution estimation (2) model compression (2) low-dimensional manifold (2) overparameterized network (2) pre-trained language model (2) convolutional neural network (2) neural network optimization (2) lottery ticket hypothesis (2) optimal transport (2) diffusion model (2) neural network (2)

Papers

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality ICLR 2025
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data ICLR 2025
Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis AISTATS 2024
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight ICLR 2024
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds JMLR 2024
Theoretical insights for diffusion guidance: A case study for Gaussian mixture models ICML 2024
Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling ICML 2024
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks NIPS 2024
A Theoretical Perspective for Speculative Decoding Algorithm NIPS 2024
Gradient Guidance for Diffusion Models: An Optimization Perspective NIPS 2024
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces JMLR 2024
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement NIPS 2023
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning ICLR 2023
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories ICML 2023
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations NIPS 2023
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks ICLR 2023
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data ICML 2023
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect ICLR 2022
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds NIPS 2022
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint ICML 2022
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks ICML 2021
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization ACL 2021
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization IJCNLP 2021
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL NIPS 2021
How Important is the Train-Validation Split in Meta-Learning? ICML 2021
On Computation and Generalization of Generative Adversarial Imitation Learning ICLR 2020
On Generalization Bounds of a Family of Recurrent Neural Networks AISTATS 2020
Towards Understanding Hierarchical Learning: Benefits of Neural Representations NIPS 2020
Differentiable Top-k with Optimal Transport NIPS 2020
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds NIPS 2019
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control ICLR 2019
Towards Understanding the Importance of Shortcut Connections in Residual Networks NIPS 2019
On Scalable and Efficient Computation of Large Scale Optimal Transport ICML 2019
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization NIPS 2018