378 citations · 2.2k across the 69 of their papers we have counts for
74 papers
A Lightweight Transformer for Faster and Robust EBSD Data Collection
Harry Dong, Sean Donegan, Megna Shah +1
Three dimensional electron back-scattered diffraction (EBSD) microscopy is a critical tool in many applications in materials science, yet its data quality can fluctuate greatly dur…
Offline Reinforcement Learning with On-Policy Q-Function Regularization
Laixi Shi, Robert Dadashi, Yuejie Chi +2
The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history d…
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Wenhao Ding, Laixi Shi, Yuejie Chi +1
Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks. In t…
Understanding Masked Autoencoders via Hierarchical Latent Variable Models
Lingjing Kong, Martin Q. Ma, Guangyi Chen +4
Masked autoencoder (MAE), a simple and effective self-supervised learning framework based on the reconstruction of masked image regions, has recently achieved prominent success in…
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
Gen Li, Yuting Wei, Yuxin Chen +1
Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling. Whil…
Identification of Nonlinear Latent Hierarchical Models
Lingjing Kong, Biwei Huang, Feng Xie +3
Identifying latent variables and causal structures from observational data is essential to many real-world applications involving biological data, medical data, and unstructured da…
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
Gen Li, Wenhao Zhan, Jason D. Lee +2
This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment.…
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression
Boyue Li, Yuejie Chi
Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique…
High-probability sample complexities for policy evaluation with linear function approximation
Gen Li, Weichen Wu, Yuejie Chi +3
This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes. We investigate the sample…
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
Laixi Shi, Gen Li, Yuting Wei +3
This paper investigates model robustness in reinforcement learning (RL) to reduce the sim-to-real gap in practice. We adopt the framework of distributionally robust Markov decision…
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
Jiin Woo, Gauri Joshi, Yuejie Chi
When the data used for reinforcement learning (RL) are collected by multiple agents in a distributed manner, federated versions of RL algorithms allow collaborative learning withou…
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Xingyu Xu, Yandi Shen, Yuejie Chi +1
We propose $\textsf{ScaledGD($λ$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is po…
Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods
Gen Li, Yanxi Chen, Yu Huang +3
Efficient computation of the optimal transport distance between two distributions serves as an algorithm subroutine that empowers various applications. This paper develops a scalab…
Deep Unfolded Tensor Robust PCA with Self-supervised Learning
Harry Dong, Megna Shah, Sean Donegan +1
Tensor robust principal component analysis (RPCA), which seeks to separate a low-rank tensor from its sparse corruptions, has been crucial in data science and machine learning wher…
Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Ruicheng Ao, Shicong Cen, Yuejie Chi
Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strate…
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Shicong Cen, Yuejie Chi, Simon S. Du +1
Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to interact in a shared dynamic environment -- permeates across a wide range of critical applications. Whil…
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Gen Li, Yuejie Chi, Yuting Wei +1
This paper studies multi-agent reinforcement learning in Markov games, with the goal of learning Nash equilibria or coarse correlated equilibria (CCE) sample-optimally. All prior r…
Local Geometry of Nonconvex Spike Deconvolution from Low-Pass Measurements
Maxime Ferreira Da Costa, Yuejie Chi
Spike deconvolution is the problem of recovering the point sources from their convolution with a known point spread function, which plays a fundamental role in many sensing and ima…
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Laixi Shi, Yuejie Chi
This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history…
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
Zhize Li, Haoyu Zhao, Boyue Li +1
To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient…
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
Harry Dong, Tian Tong, Cong Ma +1
An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than…
Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization
Shicong Cen, Fan Chen, Yuejie Chi
A major challenge in multi-agent systems is that the system complexity grows dramatically with the number of agents as well as the size of their action spaces, which is typical in…
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
Gen Li, Laixi Shi, Yuxin Chen +2
This paper is concerned with offline reinforcement learning (RL), which learns using pre-collected data without further exploration. Effective offline RL would be able to accommoda…
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Laixi Shi, Gen Li, Yuting Wei +2
Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage…