b'Wen Sun'

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...

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...

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...

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...

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...

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...

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...

Masatoshi Uehara, et al. ∙

research

∙ 06/11/2021

Corruption-Robust Offline Reinforcement Learning

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

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...

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...

Simon S. Du, et al. ∙

research

∙ 03/03/2021

Fairness of Exposure in Stochastic Bandits

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

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...

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 ...

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...

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...

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...

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...

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...

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...

Alekh Agarwal, et al. ∙

research

∙ 06/14/2020

Provably Efficient Model-based Policy Adaptation

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

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...

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...

Yingying Deng, et al. ∙

research

∙ 03/31/2020

Exploration in Action Space

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

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...

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...

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...

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...

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...

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...

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...

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...

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 ...

Wen Sun, et al. ∙

research

∙ 07/17/2018

Contextual Memory Trees

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

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...

Wen Sun, et al. ∙

research

∙ 05/28/2018

Dual Policy Iteration

Recently, a novel class of Approximate Policy Iteration (API) algorithms...

Wen Sun, et al. ∙

research

∙ 03/05/2018

Recurrent Predictive State Policy Networks

We introduce Recurrent Predictive State Policy (RPSP) networks, a recurr...

Ahmed Hefny, et al. ∙

research

∙ 12/27/2017

Sketching for Kronecker Product Regression and P-splines

TensorSketch is an oblivious linear sketch introduced in Pagh'13 and lat...

Huaian Diao, et al. ∙

research

∙ 09/25/2017

Predictive-State Decoders: Encoding the Future into Recurrent Networks

Recurrent neural networks (RNNs) are a vital modeling technique that rel...

Arun Venkatraman, et al. ∙

research

∙ 10/17/2016

Risk-Aware Algorithms for Adversarial Contextual Bandits

In this work we consider adversarial contextual bandits with risk constr...

Wen Sun, et al. ∙

Wen Sun

Featured Co-authors