Wen Sun

According to our database, Wen Sun authored at least 69 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL.

Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun

CoRR, 2024

More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning.

Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun

CoRR, 2024

2023

Provably Efficient CVaR RL in Low-rank MDPs.

Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee

CoRR, 2023

Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees.

Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun

CoRR, 2023

Making RL with Preference-based Feedback Efficient via Randomization.

Runzhe Wu, Wen Sun

CoRR, 2023

Contextual Bandits and Imitation Learning via Preference-Based Active Queries.

Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

CoRR, 2023

JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning.

Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun

CoRR, 2023

How to Query Human Feedback Efficiently in RL?

Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee

CoRR, 2023

Provable Offline Reinforcement Learning with Human Feedback.

Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun

CoRR, 2023

Refined Value-Based Offline RL under Realizability and Partial Coverage.

Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun

CoRR, 2023

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

Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage.

Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Selective Sampling and Imitation Learning via Online Regression.

Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Contextual Bandits and Imitation Learning with Preference-Based Active Queries.

Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distributional Offline Policy Evaluation with Predictive Error Guarantees.

Runzhe Wu, Masatoshi Uehara, Wen Sun

Proceedings of the International Conference on Machine Learning, 2023

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

Kaiwen Wang, Nathan Kallus, Wen Sun

Proceedings of the International Conference on Machine Learning, 2023

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

Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun

Proceedings of the International Conference on Machine Learning, 2023

PAC Reinforcement Learning for Predictive State Representations.

Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Hybrid RL: Using both offline and online data can make RL efficient.

Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Provable Benefits of Representational Transfer in Reinforcement Learning.

Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems.

Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Effectiveness of Iterative Learning Control.

Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell

Proceedings of the Learning for Dynamics and Control Conference, 2022

Online No-regret Model-Based Meta RL for Personalized Navigation.

Yuda Song, Ye Yuan, Wen Sun, Kris Kitani

Proceedings of the Learning for Dynamics and Control Conference, 2022

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

Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun

Proceedings of the International Conference on Machine Learning, 2022

Learning Bellman Complete Representations for Offline Policy Evaluation.

Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun

Proceedings of the International Conference on Machine Learning, 2022

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

Masatoshi Uehara, Xuezhou Zhang, Wen Sun

Proceedings of the Tenth International Conference on Learning Representations, 2022

Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage.

Masatoshi Uehara, Wen Sun

Proceedings of the Tenth International Conference on Learning Representations, 2022

Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design.

Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris M. Kitani

Proceedings of the Tenth International Conference on Learning Representations, 2022

Corruption-robust Offline Reinforcement Learning.

Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

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

Masatoshi Uehara, Wen Sun

CoRR, 2021

Corruption-Robust Offline Reinforcement Learning.

Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun

CoRR, 2021

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

Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

CoRR, 2021

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

Rahul Kidambi, Jonathan D. Chang, Wen Sun

CoRR, 2021

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

Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie

CoRR, 2021

Imitation Learning as f-Divergence Minimization.

Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha S. Srinivasa

Proceedings of the Algorithmic Foundations of Robotics XIV, 2021

MobILE: Model-Based Imitation Learning From Observation Alone.

Rahul Kidambi, Jonathan D. Chang, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage.

Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Robust Policy Gradient against Strong Data Corruption.

Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Yuda Song, Wen Sun

Proceedings of the 38th International Conference on Machine Learning, 2021

Bilinear Classes: A Structural Framework for Provable Generalization in RL.

Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Exploration in Action Space.

Anirudh Vemula, Wen Sun, J. Andrew Bagnell

CoRR, 2020

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs.

Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably Efficient Model-based Policy Adaptation.

Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Towards Generalization and Efficiency in Reinforcement Learning.

Wen Sun

PhD thesis, 2019

Policy Poisoning in Batch Reinforcement Learning and Control.

Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu

CoRR, 2019

Policy Poisoning in Batch Reinforcement Learning and Control.

Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Provably Efficient Imitation Learning from Observation Alone.

Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell

Proceedings of the 36th International Conference on Machine Learning, 2019

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches.

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Conference on Learning Theory, 2019

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

Anirudh Vemula, Wen Sun, J. Andrew Bagnell

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Model-Based Reinforcement Learning in Contextual Decision Processes.

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2018

Dual Policy Iteration.

Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Recurrent Predictive State Policy Networks.

Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha S. Srinivasa, Geoffrey J. Gordon

Proceedings of the 35th International Conference on Machine Learning, 2018

Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning.

Wen Sun, J. Andrew Bagnell, Byron Boots

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Predictive-State Decoders: Encoding the Future into Recurrent Networks.

Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction.

Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

Proceedings of the 34th International Conference on Machine Learning, 2017

Gradient Boosting on Stochastic Data Streams.

Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty.

Wen Sun, Jur van den Berg, Ron Alterovitz

IEEE Trans Autom. Sci. Eng., 2016

Learning to Smooth with Bidirectional Predictive State Inference Machines.

Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Inference Machines for Nonparametric Filter Learning.

Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees.

Wen Sun, J. Andrew Bagnell

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Learning to Filter with Predictive State Inference Machines.

Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell

Proceedings of the 33nd International Conference on Machine Learning, 2016

Online Instrumental Variable Regression with Applications to Online Linear System Identification.

Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning.

Wen Sun, Sachin Patil, Ron Alterovitz

IEEE Trans. Robotics, 2015

Online Bellman Residual Algorithms with Predictive Error Guarantees.

Wen Sun, J. Andrew Bagnell

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Motion planning under uncertainty for medical needle steering using optimization in belief space.

Wen Sun, Ron Alterovitz

Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Motion planning for paramagnetic microparticles under motion and sensing uncertainty.

Wen Sun, Islam S. M. Khalil, Sarthak Misra, Ron Alterovitz

Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

2013

Safe Motion Planning for Imprecise Robotic Manipulators by Minimizing Probability of Collision.

Wen Sun, Luis G. Torres, Jur van den Berg, Ron Alterovitz

Proceedings of the Robotics Research, 2013