b'Lin F. Yang'

research

∙ 09/18/2023

Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning

Decentralized exchanges (DEXs) are a cornerstone of decentralized financ...

0 Haochen Zhang, et al. ∙

research

∙ 07/11/2023

Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing

Recently, DARPA launched the ShELL program, which aims to explore how ex...

0 Sanae Amani, et al. ∙

research

∙ 06/12/2023

Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds

While numerous works have focused on devising efficient algorithms for r...

0 Jiayi Huang, et al. ∙

research

∙ 06/02/2023

MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models

Large-scale language models have shown the ability to adapt to a new tas...

0 Masoud Monajatipoor, et al. ∙

research

∙ 05/31/2023

Replicability in Reinforcement Learning

We initiate the mathematical study of replicability as an algorithmic pr...

0 Amin Karbasi, et al. ∙

research

∙ 04/18/2023

Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning

An appropriate reward function is of paramount importance in specifying ...

0 Dingwen Kong, et al. ∙

research

∙ 03/29/2023

Does Sparsity Help in Learning Misspecified Linear Bandits?

Recently, the study of linear misspecified bandits has generated intrigu...

0 Jialin Dong, et al. ∙

research

∙ 12/01/2022

Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP

This work considers the sample complexity of obtaining an ε-optimal poli...

0 Jinghan Wang, et al. ∙

research

∙ 11/08/2022

Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms

In this paper, we address the stochastic contextual linear bandit proble...

0 Osama A. Hanna, et al. ∙

research

∙ 06/13/2022

Near-Optimal Sample Complexity Bounds for Constrained MDPs

In contrast to the advances in characterizing the sample complexity for ...

4 Sharan Vaswani, et al. ∙

research

∙ 06/08/2022

Learning in Distributed Contextual Linear Bandits Without Sharing the Context

Contextual linear bandits is a rich and theoretically important model th...

0 Osama A. Hanna, et al. ∙

research

∙ 06/01/2022

Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation

We study lifelong reinforcement learning (RL) in a regret minimization s...

0 Sanae Amani, et al. ∙

research

∙ 05/26/2022

Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost

We study distributed contextual linear bandits with stochastic contexts,...

0 Sanae Amani, et al. ∙

research

∙ 11/11/2021

Solving Multi-Arm Bandit Using a Few Bits of Communication

The multi-armed bandit (MAB) problem is an active learning framework tha...

0 Osama A. Hanna, et al. ∙

research

∙ 11/01/2021

Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning

Recently there is a surge of interest in understanding the horizon-depen...

0 Yuanzhi Li, et al. ∙

research

∙ 10/26/2021

Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs

Despite a large amount of effort in dealing with heavy-tailed error in m...

0 Han Zhong, et al. ∙

research

∙ 10/12/2021

On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) algorithms often suffer from a...

0 Weichao Mao, et al. ∙

research

∙ 10/09/2021

Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation

Recently, deep reinforcement learning (RL) has achieved remarkable empir...

0 Junhong Shen, et al. ∙

research

∙ 10/07/2021

Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver

Although model-based reinforcement learning (RL) approaches are consider...

0 Xiaoyu Chen, et al. ∙

research

∙ 08/11/2021

Gap-Dependent Unsupervised Exploration for Reinforcement Learning

For the problem of task-agnostic reinforcement learning (RL), an agent f...

8 Jingfeng Wu, et al. ∙

research

∙ 06/15/2021

Randomized Exploration for Reinforcement Learning with General Value Function Approximation

We propose a model-free reinforcement learning algorithm inspired by the...

0 Haque Ishfaq, et al. ∙

research

∙ 06/14/2021

Online Sub-Sampling for Reinforcement Learning with General Function Approximation

Designing provably efficient algorithms with general function approximat...

0 Dingwen Kong, et al. ∙

research

∙ 06/11/2021

Safe Reinforcement Learning with Linear Function Approximation

Safety in reinforcement learning has become increasingly important in re...

0 Sanae Amani, et al. ∙

research

∙ 06/11/2021

Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs

Graph neural networks (GNNs) are powerful tools for learning from graph ...

0 Jialin Dong, et al. ∙

research

∙ 03/22/2021

Provably Correct Optimization and Exploration with Non-linear Policies

Policy optimization methods remain a powerful workhorse in empirical Rei...

1 Fei Feng, et al. ∙

research

∙ 02/25/2021

Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally

We study the statistical limits of Imitation Learning (IL) in episodic M...

0 Nived Rajaraman, et al. ∙

research

∙ 01/02/2021

A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost

Many real-world applications, such as those in medical domains, recommen...

0 Minbo Gao, et al. ∙

research

∙ 11/29/2020

Minimax Sample Complexity for Turn-based Stochastic Game

The empirical success of Multi-agent reinforcement learning is encouragi...

0 Qiwen Cui, et al. ∙

research

∙ 11/25/2020

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning

In this paper we consider multi-objective reinforcement learning where t...

3 Jingfeng Wu, et al. ∙

research

∙ 11/03/2020

Episodic Linear Quadratic Regulators with Low-rank Transitions

Linear Quadratic Regulators (LQR) achieve enormous successful real-world...

0 Tianyu Wang, et al. ∙

research

∙ 11/03/2020

Random Walk Bandits

Bandit learning problems find important applications ranging from medica...

0 Tianyu Wang, et al. ∙

research

∙ 10/12/2020

Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?

It is believed that a model-based approach for reinforcement learning (R...

0 Qiwen Cui, et al. ∙

research

∙ 09/13/2020

Toward the Fundamental Limits of Imitation Learning

Imitation learning (IL) aims to mimic the behavior of an expert policy i...

7 Nived Rajaraman, et al. ∙

research

∙ 08/15/2020

Obtaining Adjustable Regularization for Free via Iterate Averaging

Regularization for optimization is a crucial technique to avoid overfitt...

0 Jingfeng Wu, et al. ∙

research

∙ 07/15/2020

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

Model-based reinforcement learning (RL), which finds an optimal policy u...

27 Kaiqing Zhang, et al. ∙

research

∙ 06/19/2020

On Reward-Free Reinforcement Learning with Linear Function Approximation

Reward-free reinforcement learning (RL) is a framework which is suitable...

0 Ruosong Wang, et al. ∙

research

∙ 06/16/2020

Q-learning with Logarithmic Regret

This paper presents the first non-asymptotic result showing that a model...

0 Kunhe Yang, et al. ∙

research

∙ 06/16/2020

Preference-based Reinforcement Learning with Finite-Time Guarantees

Preference-based Reinforcement Learning (PbRL) replaces reward values in...

0 Yichong Xu, et al. ∙

research

∙ 06/01/2020

Model-Based Reinforcement Learning with Value-Targeted Regression

This paper studies model-based reinforcement learning (RL) for regret mi...

11 Alex Ayoub, et al. ∙

research

∙ 05/21/2020

Provably Efficient Reinforcement Learning with General Value Function Approximation

Value function approximation has demonstrated phenomenal empirical succe...

13 Ruosong Wang, et al. ∙

research

∙ 05/01/2020

Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?

Learning to plan for long horizons is a central challenge in episodic re...

7 Ruosong Wang, et al. ∙

research

∙ 03/15/2020

Provably Efficient Exploration for RL with Unsupervised Learning

We study how to use unsupervised learning for efficient exploration in r...

2 Fei Feng, et al. ∙

research

∙ 02/23/2020

Sketching Transformed Matrices with Applications to Natural Language Processing

Suppose we are given a large matrix A=(a_i,j) that cannot be stored in m...

0 Yingyu Liang, et al. ∙

research

∙ 12/06/2019

Does Knowledge Transfer Always Help to Learn a Better Policy?

One of the key approaches to save samples when learning a policy for a r...

21 Fei Feng, et al. ∙

research

∙ 10/30/2019

Continuous Control with Contexts, Provably

A fundamental challenge in artificial intelligence is to build an agent ...

0 Simon S. Du, et al. ∙

research

∙ 10/07/2019

Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?

Modern deep learning methods provide an effective means to learn good re...

15 Simon S. Du, et al. ∙

research

∙ 10/04/2019

Efficient Symmetric Norm Regression via Linear Sketching

We provide efficient algorithms for overconstrained linear regression pr...

0 Zhao Song, et al. ∙

research

∙ 08/29/2019

Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity

In this paper, we settle the sampling complexity of solving discounted t...

12 Aaron Sidford, et al. ∙

research

∙ 06/10/2019

On the Optimality of Sparse Model-Based Planning for Markov Decision Processes

This work considers the sample complexity of obtaining an ϵ-optimal poli...

0 Alekh Agarwal, et al. ∙

research

∙ 06/02/2019

Feature-Based Q-Learning for Two-Player Stochastic Games

Consider a two-player zero-sum stochastic game where the transition func...

0 Zeyu Jia, et al. ∙

Lin F. Yang

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