Sharp minimax bounds for testing discrete monotone distributions. - Yuting Wei 001; Martin J. Wainwright (ISIT, 2016)

The geometry of hypothesis testing over convex cones: Generalized likelihood tests and minimax radii. - Yuting Wei 001; Martin J. Wainwright; Adityanand Guntuboyina (CoRR, 2017)

The local geometry of testing in ellipses: Tight control via localized Kolomogorov widths. - Yuting Wei 001; Martin J. Wainwright (CoRR, 2017)

From Gauss to Kolmogorov: Localized Measures of Complexity for Ellipses. - Yuting Wei 001; Billy Fang; Martin J. Wainwright (CoRR, 2018)

Early Stopping for Kernel Boosting Algorithms: A General Analysis With Localized Complexities. - Yuting Wei 001; Fanny Yang; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2019)

Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification. - Chen Dan 001; Yuting Wei 001; Pradeep Ravikumar (ICML, 2020)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. - Gen Li 005; Yuting Wei 001; Yuejie Chi; Yuantao Gu; Yuxin Chen 002 (NeurIPS, 2020)

Randomized tests for high-dimensional regression: A more efficient and powerful solution. - Yue Li; Ilmun Kim; Yuting Wei 001 (NeurIPS, 2020)

Inference for linear forms of eigenvectors under minimal eigenvalue separation: Asymmetry and heteroscedasticity. - Chen Cheng; Yuting Wei 001; Yuxin Chen 002 (CoRR, 2020)

Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification. - Chen Dan 001; Yuting Wei 001; Pradeep Ravikumar (CoRR, 2020)

The Lasso with general Gaussian designs with applications to hypothesis testing. - Michael Celentano; Andrea Montanari; Yuting Wei 001 (CoRR, 2020)

The Local Geometry of Testing in Ellipses: Tight Control via Localized Kolmogorov Widths. - Yuting Wei 001; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2020)

Debiasing Evaluations That Are Biased by Evaluations. - Jingyan Wang 001; Ivan Stelmakh; Yuting Wei 001; Nihar B. Shah (AAAI, 2021)

Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. - Pratik Patil; Yuting Wei 001; Alessandro Rinaldo; Ryan J. Tibshirani (AISTATS, 2021)

Softmax Policy Gradient Methods Can Take Exponential Time to Converge. - Gen Li 005; Yuting Wei 001; Yuejie Chi; Yuantao Gu; Yuxin Chen 002 (COLT, 2021)

Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning. - Gen Li 005; Changxiao Cai; Yuxin Chen 002; Yuantao Gu; Yuting Wei 001; Yuejie Chi (ICML, 2021)

Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting. - Gen Li 005; Yuxin Chen 002; Yuejie Chi; Yuantao Gu; Yuting Wei 001 (NeurIPS, 2021)

Minimum 𝓁1-norm interpolators: Precise asymptotics and multiple descent. - Yue Li; Yuting Wei 001 (CoRR, 2021)

Tackling Small Eigen-Gaps: Fine-Grained Eigenvector Estimation and Inference Under Heteroscedastic Noise. - Chen Cheng; Yuting Wei 001; Yuxin Chen 002 (IEEE Trans. Inf. Theory, 2021)

Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity. - Laixi Shi; Gen Li 005; Yuting Wei 001; Yuxin Chen 002; Yuejie Chi (ICML, 2022)

Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model. - Gen Li 005; Yuejie Chi; Yuting Wei 001; Yuxin Chen 002 (NeurIPS, 2022)

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning. - Gen Li 005; Laixi Shi; Yuxin Chen 002; Yuejie Chi; Yuting Wei 001 (CoRR, 2022)

Mitigating multiple descents: A model-agnostic framework for risk monotonization. - Pratik Patil; Arun Kumar Kuchibhotla; Yuting Wei 001; Alessandro Rinaldo (CoRR, 2022)

A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models. - Gen Li 005; Yuting Wei 001 (CoRR, 2022)

Minimax-Optimal Multi-Agent RL in Zero-Sum Markov Games With a Generative Model. - Gen Li 005; Yuejie Chi; Yuting Wei 001; Yuxin Chen 002 (CoRR, 2022)

Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization. - Shicong Cen; Chen Cheng; Yuxin Chen 002; Yuting Wei 001; Yuejie Chi (Oper. Res., 2022)

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. - Gen Li 005; Yuting Wei 001; Yuejie Chi; Yuantao Gu; Yuxin Chen 002 (IEEE Trans. Inf. Theory, 2022)

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model. - Laixi Shi; Gen Li 005; Yuting Wei 001; Yuxin Chen 002; Matthieu Geist; Yuejie Chi (NeurIPS, 2023)

Approximate message passing from random initialization with applications to ℤ2 synchronization. - Gen Li 005; Wei Fan; Yuting Wei 001 (CoRR, 2023)

Sharp high-probability sample complexities for policy evaluation with linear function approximation. - Gen Li 005; Weichen Wu; Yuejie Chi; Cong Ma 001; Alessandro Rinaldo; Yuting Wei 001 (CoRR, 2023)

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models. - Gen Li 005; Yuting Wei 001; Yuxin Chen 002; Yuejie Chi (CoRR, 2023)

Federated Natural Policy Gradient Methods for Multi-task Reinforcement Learning. - Tong Yang; Shicong Cen; Yuting Wei 001; Yuxin Chen 002; Yuejie Chi (CoRR, 2023)

Softmax policy gradient methods can take exponential time to converge. - Gen Li 005; Yuting Wei 001; Yuejie Chi; Yuxin Chen 002 (Math. Program., 2023)

Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models. - Gen Li 005; Yuting Wei 001; Yuxin Chen 002; Yuejie Chi (ICLR, 2024)

Accelerating Convergence of Score-Based Diffusion Models, Provably. - Gen Li 005; Yu Huang 023; Timofey Efimov; Yuting Wei 001; Yuejie Chi; Yuxin Chen 002 (ICML, 2024)

Theoretical insights for diffusion guidance: A case study for Gaussian mixture models. - Yuchen Wu; Minshuo Chen; Zihao Li; Mengdi Wang; Yuting Wei 001 (ICML, 2024)

Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs. - Kevin Tan; Wei Fan; Yuting Wei 001 (NeurIPS, 2024)

Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning. - Tong Yang; Shicong Cen; Yuting Wei 001; Yuxin Chen 002; Yuejie Chi (NeurIPS, 2024)

A non-asymptotic distributional theory of approximate message passing for sparse and robust regression. - Gen Li 005; Yuting Wei 001 (CoRR, 2024)

A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models. - Gen Li 005; Yuting Wei 001; Yuejie Chi; Yuxin Chen 002 (CoRR, 2024)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis. - Gen Li 005; Changxiao Cai; Yuxin Chen 002; Yuting Wei 001; Yuejie Chi (Oper. Res., 2024)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. - Gen Li 005; Yuting Wei 001; Yuejie Chi; Yuxin Chen 002 (Oper. Res., 2024)

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization. - Shicong Cen; Yuting Wei 001; Yuejie Chi (J. Mach. Learn. Res., 2024)

High-Probability Sample Complexities for Policy Evaluation With Linear Function Approximation. - Gen Li 005; Weichen Wu; Yuejie Chi; Cong Ma 001; Alessandro Rinaldo; Yuting Wei 001 (IEEE Trans. Inf. Theory, 2024)

Towards a mathematical theory for consistency training in diffusion models. - Gen Li 005; Zhihan Huang; Yuting Wei 001 (AISTATS, 2025)

Statistical and Algorithmic Foundations of Reinforcement Learning. - Yuejie Chi; Yuxin Chen 002; Yuting Wei 001 (CoRR, 2025)