Yuting Wei

Yuejie Chi

J. Mach. Learn. Res., 2024

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model.

[BibT_eX]

[DOI]

Oper. Res., 2024

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis.

[BibT_eX]

[DOI]

Oper. Res., 2024

Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs.

[BibT_eX]

[DOI]

Kevin Tan

Wei Fan

CoRR, 2024

A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2024

Towards a mathematical theory for consistency training in diffusion models.

[BibT_eX]

[DOI]

Zhihan Huang

CoRR, 2024

A non-asymptotic distributional theory of approximate message passing for sparse and robust regression.

[BibT_eX]

[DOI]

CoRR, 2024

Theoretical insights for diffusion guidance: A case study for Gaussian mixture models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Accelerating Convergence of Score-Based Diffusion Models, Provably.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Softmax policy gradient methods can take exponential time to converge.

[BibT_eX]

[DOI]

Math. Program., 2023

Federated Natural Policy Gradient Methods for Multi-task Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models.

[BibT_eX]

[DOI]

CoRR, 2023

Sharp high-probability sample complexities for policy evaluation with linear function approximation.

[BibT_eX]

[DOI]

CoRR, 2023

Approximate message passing from random initialization with applications to ℤ<sub>2</sub> synchronization.

[BibT_eX]

[DOI]

Wei Fan

CoRR, 2023

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2022

Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization.

[BibT_eX]

[DOI]

Oper. Res., 2022

Minimax-Optimal Multi-Agent RL in Zero-Sum Markov Games With a Generative Model.

[BibT_eX]

[DOI]

CoRR, 2022

A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models.

[BibT_eX]

[DOI]

CoRR, 2022

Mitigating multiple descents: A model-agnostic framework for risk monotonization.

[BibT_eX]

[DOI]

Pratik Patil

Arun Kumar Kuchibhotla

Alessandro Rinaldo

CoRR, 2022

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Tackling Small Eigen-Gaps: Fine-Grained Eigenvector Estimation and Inference Under Heteroscedastic Noise.

[BibT_eX]

[DOI]

Chen Cheng

Yuxin Chen

IEEE Trans. Inf. Theory, 2021

Minimum 𝓁<sub>1</sub>-norm interpolators: Precise asymptotics and multiple descent.

[BibT_eX]

[DOI]

Yue Li

CoRR, 2021

Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Softmax Policy Gradient Methods Can Take Exponential Time to Converge.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2021

Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Debiasing Evaluations That Are Biased by Evaluations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

The Local Geometry of Testing in Ellipses: Tight Control via Localized Kolmogorov Widths.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2020

The Lasso with general Gaussian designs with applications to hypothesis testing.

[BibT_eX]

[DOI]

Michael Celentano

Andrea Montanari

CoRR, 2020

Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification.

[BibT_eX]

[DOI]

Chen Dan

Pradeep Ravikumar

CoRR, 2020

Inference for linear forms of eigenvectors under minimal eigenvalue separation: Asymmetry and heteroscedasticity.

[BibT_eX]

[DOI]

Chen Cheng

Yuxin Chen

CoRR, 2020

Randomized tests for high-dimensional regression: A more efficient and powerful solution.

[BibT_eX]

[DOI]

Yue Li

Ilmun Kim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification.

[BibT_eX]

[DOI]

Chen Dan

Pradeep Ravikumar

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Early Stopping for Kernel Boosting Algorithms: A General Analysis With Localized Complexities.

[BibT_eX]

[DOI]

Fanny Yang

IEEE Trans. Inf. Theory, 2019

2018

From Gauss to Kolmogorov: Localized Measures of Complexity for Ellipses.

[BibT_eX]

[DOI]

Billy Fang

CoRR, 2018

2017

The local geometry of testing in ellipses: Tight control via localized Kolomogorov widths.

[BibT_eX]

[DOI]

CoRR, 2017

The geometry of hypothesis testing over convex cones: Generalized likelihood tests and minimax radii.

[BibT_eX]

[DOI]

Adityanand Guntuboyina

CoRR, 2017

2016

Sharp minimax bounds for testing discrete monotone distributions.

[BibT_eX]

[DOI]