b'Wen Sun'

research

∙ 05/29/2023

How to Query Human Feedback Efficiently in RL?

Reinforcement Learning with Human Feedback (RLHF) is a paradigm in which...

0 Wenhao Zhan, et al. ∙

research

∙ 05/25/2023

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

While distributional reinforcement learning (RL) has demonstrated empiri...

0 Kaiwen Wang, et al. ∙

research

∙ 05/24/2023

Provable Offline Reinforcement Learning with Human Feedback

In this paper, we investigate the problem of offline reinforcement learn...

0 Wenhao Zhan, et al. ∙

research

∙ 02/19/2023

Distributional Offline Policy Evaluation with Predictive Error Guarantees

We study the problem of estimating the distribution of the return of a p...

0 Runzhe Wu, et al. ∙

research

∙ 02/09/2023

Multi-task Representation Learning for Pure Exploration in Linear Bandits

Despite the recent success of representation learning in sequential deci...

0 Yihan Du, et al. ∙

research

∙ 02/07/2023

Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR

In this paper, we study risk-sensitive Reinforcement Learning (RL), focu...

0 Kaiwen Wang, et al. ∙

research

∙ 02/05/2023

Refined Value-Based Offline RL under Realizability and Partial Coverage

In offline reinforcement learning (RL) we have no opportunity to explore...

0 Masatoshi Uehara, et al. ∙

research

∙ 10/13/2022

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

We consider a hybrid reinforcement learning setting (Hybrid RL), in whic...

20 Yuda Song, et al. ∙

research

∙ 07/29/2022

Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions

Reinforcement Learning (RL) and continuous nonlinear control have been s...

6 Wenhao Luo, et al. ∙

research

∙ 07/26/2022

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

We study off-policy evaluation (OPE) for partially observable MDPs (POMD...

3 Masatoshi Uehara, et al. ∙

research

∙ 07/12/2022

Learning Bellman Complete Representations for Offline Policy Evaluation

We study representation learning for Offline Reinforcement Learning (RL)...

6 Jonathan D. Chang, et al. ∙

research

∙ 07/12/2022

PAC Reinforcement Learning for Predictive State Representations

In this paper we study online Reinforcement Learning (RL) in partially o...

5 Wenhao Zhan, et al. ∙

research

∙ 06/24/2022

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings

We study reinforcement learning with function approximation for large-sc...

6 Masatoshi Uehara, et al. ∙

research

∙ 06/24/2022

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems

We study Reinforcement Learning for partially observable dynamical syste...

26 Masatoshi Uehara, et al. ∙

research

∙ 06/17/2022

Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation

Deep learning models are found to be vulnerable to adversarial examples,...

4 Wen Sun, et al. ∙

research

∙ 05/29/2022

Provable Benefits of Representational Transfer in Reinforcement Learning

We study the problem of representational transfer in RL, where an agent ...

7 Alekh Agarwal, et al. ∙

research

∙ 03/29/2022

Learning to Detect Mobile Objects from LiDAR Scans Without Labels

Current 3D object detectors for autonomous driving are almost entirely t...

33 Yurong You, et al. ∙

research

∙ 03/22/2022

Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception

Self-driving cars must detect vehicles, pedestrians, and other traffic p...

3 Yurong You, et al. ∙

research

∙ 01/31/2022

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach

We present BRIEE (Block-structured Representation learning with Interlea...

10 Xuezhou Zhang, et al. ∙

research

∙ 11/17/2021

On the Effectiveness of Iterative Learning Control

Iterative learning control (ILC) is a powerful technique for high perfor...

2 Anirudh Vemula, et al. ∙

research

∙ 10/09/2021

Representation Learning for Online and Offline RL in Low-rank MDPs

This work studies the question of Representation Learning in RL: how can...

5 Masatoshi Uehara, et al. ∙

research

∙ 07/15/2021

PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration

Model-based Reinforcement Learning (RL) is a popular learning paradigm d...

7 Yuda Song, et al. ∙

research

∙ 07/13/2021

Pessimistic Model-based Offline RL: PAC Bounds and Posterior Sampling under Partial Coverage

We study model-based offline Reinforcement Learning with general functio...

7 Masatoshi Uehara, et al. ∙

research

∙ 06/11/2021

Corruption-Robust Offline Reinforcement Learning

We study the adversarial robustness in offline reinforcement learning. G...

7 Xuezhou Zhang, et al. ∙

research

∙ 06/06/2021

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage

This paper studies offline Imitation Learning (IL) where an agent learns...

6 Jonathan D. Chang, et al. ∙

research

∙ 03/19/2021

Bilinear Classes: A Structural Framework for Provable Generalization in RL

This work introduces Bilinear Classes, a new structural framework, which...

52 Simon S. Du, et al. ∙

research

∙ 03/03/2021

Fairness of Exposure in Stochastic Bandits

Contextual bandit algorithms have become widely used for recommendation ...

2 Lequn Wang, et al. ∙

research

∙ 02/22/2021

Optimism is All You Need: Model-Based Imitation Learning From Observation Alone

This paper studies Imitation Learning from Observations alone (ILFO) whe...

13 Rahul Kidambi, et al. ∙

research

∙ 02/11/2021

Robust Policy Gradient against Strong Data Corruption

We study the problem of robust reinforcement learning under adversarial ...

7 Xuezhou Zhang, et al. ∙

research

∙ 02/05/2021

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency

We offer a theoretical characterization of off-policy evaluation (OPE) i...

2 Masatoshi Uehara, et al. ∙

research

∙ 10/25/2020

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

Industrial Internet of Things (IoT) enables distributed intelligent serv...

3 Wen Sun, et al. ∙

research

∙ 10/08/2020

Learning the Linear Quadratic Regulator from Nonlinear Observations

We introduce a new problem setting for continuous control called the LQR...

4 Zakaria Mhammedi, et al. ∙

research

∙ 07/16/2020

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

Direct policy gradient methods for reinforcement learning are a successf...

0 Alekh Agarwal, et al. ∙

research

∙ 06/22/2020

Information Theoretic Regret Bounds for Online Nonlinear Control

This work studies the problem of sequential control in an unknown, nonli...

14 Sham Kakade, et al. ∙

research

∙ 06/18/2020

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

In order to deal with the curse of dimensionality in reinforcement learn...

7 Alekh Agarwal, et al. ∙

research

∙ 06/14/2020

Provably Efficient Model-based Policy Adaptation

The high sample complexity of reinforcement learning challenges its use ...

7 Yuda Song, et al. ∙

research

∙ 06/09/2020

Constrained episodic reinforcement learning in concave-convex and knapsack settings

We propose an algorithm for tabular episodic reinforcement learning with...

8 Kianté Brantley, et al. ∙

research

∙ 05/27/2020

Arbitrary Style Transfer via Multi-Adaptation Network

Arbitrary style transfer is a significant topic with both research value...

4 Yingying Deng, et al. ∙

research

∙ 03/31/2020

Exploration in Action Space

Parameter space exploration methods with black-box optimization have rec...

5 Anirudh Vemula, et al. ∙

research

∙ 11/20/2019

Corruption Robust Exploration in Episodic Reinforcement Learning

We initiate the study of multi-stage episodic reinforcement learning und...

12 Thodoris Lykouris, et al. ∙

research

∙ 10/13/2019

Policy Poisoning in Batch Reinforcement Learning and Control

We study a security threat to batch reinforcement learning and control w...

8 Yuzhe Ma, et al. ∙

research

∙ 09/29/2019

Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

We study the Kronecker product regression problem, in which the design m...

8 Huaian Diao, et al. ∙

research

∙ 05/30/2019

Imitation Learning as f-Divergence Minimization

We address the problem of imitation learning with multi-modal demonstrat...

9 Liyiming Ke, et al. ∙

research

∙ 05/27/2019

Provably Efficient Imitation Learning from Observation Alone

We study Imitation Learning (IL) from Observations alone (ILFO) in large...

2 Wen Sun, et al. ∙

research

∙ 05/01/2019

Efficient Model-free Reinforcement Learning in Metric Spaces

Model-free Reinforcement Learning (RL) algorithms such as Q-learning [Wa...

8 Zhao Song, et al. ∙

research

∙ 03/01/2019

Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention

Cluster analysis aims at separating patients into phenotypically heterog...

4 Eryu Xia, et al. ∙

research

∙ 01/31/2019

Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

Black-box optimizers that explore in parameter space have often been sho...

4 Anirudh Vemula, et al. ∙

research

∙ 11/21/2018

Model-Based Reinforcement Learning in Contextual Decision Processes

We study the sample complexity of model-based reinforcement learning in ...

12 Wen Sun, et al. ∙

research

∙ 07/17/2018

Contextual Memory Trees

We design and study a Contextual Memory Tree (CMT), a learning memory co...

6 Wen Sun, et al. ∙

research

∙ 05/29/2018

Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning

In this paper, we propose to combine imitation and reinforcement learnin...

0 Wen Sun, et al. ∙

Wen Sun

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