b'Yuejie Chi'

research

∙ 09/18/2023

A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

Communication efficiency is a major challenge in federated learning (FL)...

0 Pedro Valdeira, et al. ∙

research

∙ 08/18/2023

A Lightweight Transformer for Faster and Robust EBSD Data Collection

Three dimensional electron back-scattered diffraction (EBSD) microscopy ...

0 Harry Dong, et al. ∙

research

∙ 07/25/2023

Offline Reinforcement Learning with On-Policy Q-Function Regularization

The core challenge of offline reinforcement learning (RL) is dealing wit...

0 Laixi Shi, et al. ∙

research

∙ 07/15/2023

Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation

Robustness has been extensively studied in reinforcement learning (RL) t...

0 Wenhao Ding, et al. ∙

research

∙ 06/15/2023

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

Diffusion models, which convert noise into new data instances by learnin...

4 Gen Li, et al. ∙

research

∙ 05/30/2023

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

This paper is concerned with the problem of policy evaluation with linea...

0 Gen Li, et al. ∙

research

∙ 05/26/2023

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

This paper investigates model robustness in reinforcement learning (RL) ...

5 Laixi Shi, et al. ∙

research

∙ 05/18/2023

The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond

When the data used for reinforcement learning (RL) are collected by mult...

0 Jiin Woo, et al. ∙

research

∙ 05/17/2023

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

This paper studies tabular reinforcement learning (RL) in the hybrid set...

1 Gen Li, et al. ∙

research

∙ 05/17/2023

Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression

Achieving communication efficiency in decentralized machine learning has...

0 Boyue Li, et al. ∙

research

∙ 02/02/2023

The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing

We propose , a preconditioned gradient descent method to tackle the low-...

0 Xingyu Xu, et al. ∙

research

∙ 01/30/2023

Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods

Efficient computation of the optimal transport distance between two dist...

1 Gen Li, et al. ∙

research

∙ 12/21/2022

Deep Unfolded Tensor Robust PCA with Self-supervised Learning

Tensor robust principal component analysis (RPCA), which seeks to separa...

0 Harry Dong, et al. ∙

research

∙ 11/16/2022

Asynchronous Gradient Play in Zero-Sum Multi-agent Games

Finding equilibria via gradient play in competitive multi-agent games ha...

0 Ruicheng Ao, et al. ∙

research

∙ 10/03/2022

Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games

Multi-Agent Reinforcement Learning (MARL) – where multiple agents learn ...

0 Shicong Cen, et al. ∙

research

∙ 08/22/2022

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

This paper studies multi-agent reinforcement learning in Markov games, w...

3 Gen Li, et al. ∙

research

∙ 08/22/2022

Local Geometry of Nonconvex Spike Deconvolution from Low-Pass Measurements

Spike deconvolution is the problem of recovering the point sources from ...

0 Maxime Ferreira Da Costa, et al. ∙

research

∙ 08/11/2022

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity

This paper concerns the central issues of model robustness and sample ef...

0 Laixi Shi, et al. ∙

research

∙ 06/20/2022

SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

To enable large-scale machine learning in bandwidth-hungry environments ...

1 Zhize Li, et al. ∙

research

∙ 06/18/2022

Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent

An increasing number of data science and machine learning problems rely ...

0 Harry Dong, et al. ∙

research

∙ 04/12/2022

Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization

A major challenge in multi-agent systems is that the system complexity g...

0 Shicong Cen, et al. ∙

research

∙ 04/11/2022

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning

This paper is concerned with offline reinforcement learning (RL), which ...

3 Gen Li, et al. ∙

research

∙ 02/28/2022

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

Offline or batch reinforcement learning seeks to learn a near-optimal po...

6 Laixi Shi, et al. ∙

research

∙ 01/31/2022

BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression

Communication efficiency has been widely recognized as the bottleneck fo...

12 Haoyu Zhao, et al. ∙

research

∙ 10/09/2021

Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning

Achieving sample efficiency in online episodic reinforcement learning (R...

5 Gen Li, et al. ∙

research

∙ 10/04/2021

DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization

Emerging applications in multi-agent environments such as internet-of-th...

4 Boyue Li, et al. ∙

research

∙ 05/31/2021

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization

This paper investigates the problem of computing the equilibrium of comp...

0 Shicong Cen, et al. ∙

research

∙ 05/24/2021

Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence

Policy optimization, which learns the policy of interest by maximizing t...

7 Wenhao Zhan, et al. ∙

research

∙ 05/17/2021

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

Low-complexity models such as linear function representation play a pivo...

6 Gen Li, et al. ∙

research

∙ 04/29/2021

Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements

Tensors, which provide a powerful and flexible model for representing mu...

0 Tian Tong, et al. ∙

research

∙ 02/26/2021

NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies

Despite being crucial to health and quality of life, sleep – especially ...

0 Harlin Lee, et al. ∙

research

∙ 02/22/2021

Softmax Policy Gradient Methods Can Take Exponential Time to Converge

The softmax policy gradient (PG) method, which performs gradient ascent ...

6 Gen Li, et al. ∙

research

∙ 02/12/2021

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

Q-learning, which seeks to learn the optimal Q-function of a Markov deci...

4 Gen Li, et al. ∙

research

∙ 01/13/2021

Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing

Low-rank matrix estimation plays a central role in various applications ...

0 Cong Ma, et al. ∙

research

∙ 12/15/2020

Spectral Methods for Data Science: A Statistical Perspective

Spectral methods have emerged as a simple yet surprisingly effective app...

18 Yuxin Chen, et al. ∙

research

∙ 10/26/2020

Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number

Many problems in data science can be treated as estimating a low-rank ma...

0 Tian Tong, et al. ∙

research

∙ 10/20/2020

Compressed Super-Resolution of Positive Sources

Atomic norm minimization is a convex optimization framework to recover p...

0 Maxime Ferreira Da Costa, et al. ∙

research

∙ 07/13/2020

Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization

Natural policy gradient (NPG) methods are among the most widely used pol...

9 Shicong Cen, et al. ∙

research

∙ 06/04/2020

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

Asynchronous Q-learning aims to learn the optimal action-value function ...

11 Gen Li, et al. ∙

research

∙ 05/26/2020

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

We investigate the sample efficiency of reinforcement learning in a γ-di...

4 Gen Li, et al. ∙

research

∙ 05/18/2020

Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent

Low-rank matrix estimation is a canonical problem that finds numerous ap...

0 Tian Tong, et al. ∙

research

∙ 11/25/2019

Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently

Multi-channel sparse blind deconvolution, or convolutional sparse coding...

0 Laixi Shi, et al. ∙

research

∙ 10/09/2019

Subspace Estimation from Unbalanced and Incomplete Data Matrices: ℓ_2,∞ Statistical Guarantees

This paper is concerned with estimating the column space of an unknown l...

0 Changxiao Cai, et al. ∙

research

∙ 10/03/2019

On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution

The stability of spike deconvolution, which aims at recovering point sou...

0 Maxime Ferreira Da Costa, et al. ∙

research

∙ 09/12/2019

Communication-Efficient Distributed Optimization in Networks with Gradient Tracking

There is a growing interest in large-scale machine learning and optimiza...

0 Boyue Li, et al. ∙

research

∙ 05/29/2019

Vector-Valued Graph Trend Filtering with Non-Convex Penalties

We study the denoising of piecewise smooth graph signals that exhibit in...

0 Rohan Varma, et al. ∙

research

∙ 05/29/2019

Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data

Stochastic variance reduced methods have gained a lot of interest recent...

0 Shicong Cen, et al. ∙

research

∙ 04/08/2019

Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution

Convex optimization recently emerges as a compelling framework for perfo...

0 Yuejie Chi, et al. ∙

research

∙ 02/20/2019

Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

This paper studies noisy low-rank matrix completion: given partial and c...

0 Yuxin Chen, et al. ∙

research

∙ 09/25/2018

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

Substantial progress has been made recently on developing provably accur...

0 Yuejie Chi, et al. ∙

Yuejie Chi

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