Wen Sun

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 79

Top Topics

Markov Decision Processes

Reinforcement Learning

Motion Planning

Top Venues

Publications

Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. CoRR (2024)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
Provable Reward-Agnostic Preference-Based Reinforcement Learning. ICLR (2024)
Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun
Dataset Reset Policy Optimization for RLHF. CoRR (2024)
Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. CoRR (2024)
Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. CoRR (2024)
Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. ICLR (2024)
Runzhe Wu, Wen Sun
Making RL with Preference-based Feedback Efficient via Randomization. ICLR (2024)
Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. CoRR (2024)
Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley, Wen Sun
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation. CoRR (2024)
Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun
Adversarial Imitation Learning via Boosting. CoRR (2024)
Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun
Adversarial Imitation Learning via Boosting. ICLR (2024)
Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. CoRR (2024)
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
Provably Efficient CVaR RL in Low-rank MDPs. ICLR (2024)
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Provable Offline Preference-Based Reinforcement Learning. ICLR (2024)
Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun
Understanding Preference Fine-Tuning Through the Lens of Coverage. CoRR (2024)
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun
REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR (2024)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Contextual Bandits and Imitation Learning via Preference-Based Active Queries. CoRR (2023)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Contextual Bandits and Imitation Learning with Preference-Based Active Queries. NeurIPS (2023)
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
Provably Efficient CVaR RL in Low-rank MDPs. CoRR (2023)
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. CoRR (2023)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Selective Sampling and Imitation Learning via Online Regression. CoRR (2023)
Runzhe Wu, Wen Sun
Making RL with Preference-based Feedback Efficient via Randomization. CoRR (2023)
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR (2023)
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. NeurIPS (2023)
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS (2023)
Kaiwen Wang, Nathan Kallus, Wen Sun
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. ICML (2023)
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Provable Offline Reinforcement Learning with Human Feedback. CoRR (2023)
Runzhe Wu, Masatoshi Uehara, Wen Sun
Distributional Offline Policy Evaluation with Predictive Error Guarantees. CoRR (2023)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Selective Sampling and Imitation Learning via Online Regression. NeurIPS (2023)
Runzhe Wu, Masatoshi Uehara, Wen Sun
Distributional Offline Policy Evaluation with Predictive Error Guarantees. ICML (2023)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
PAC Reinforcement Learning for Predictive State Representations. ICLR (2023)
Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun
Hybrid RL: Using both offline and online data can make RL efficient. ICLR (2023)
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. NeurIPS (2023)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
How to Query Human Feedback Efficiently in RL? CoRR (2023)
Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. CoRR (2023)
Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. CoRR (2023)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML (2023)
Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang
Provable Benefits of Representational Transfer in Reinforcement Learning. COLT (2023)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
PAC Reinforcement Learning for Predictive State Representations. CoRR (2022)
Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
On the Effectiveness of Iterative Learning Control. L4DC (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR (2022)
Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. ICML (2022)
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS (2022)
Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR (2022)
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris M. Kitani
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. ICLR (2022)
Yuda Song, Ye Yuan, Wen Sun, Kris Kitani
Online No-regret Model-Based Meta RL for Personalized Navigation. L4DC (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR (2022)
Masatoshi Uehara, Wen Sun
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. ICLR (2022)
Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang
Provable Benefits of Representational Transfer in Reinforcement Learning. CoRR (2022)
Yuda Song, Ye Yuan, Wen Sun, Kris Kitani
Online No-regret Model-Based Meta RL for Personalized Navigation. CoRR (2022)
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. NeurIPS (2021)
Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha S. Srinivasa
Imitation Learning as f-Divergence Minimization. WAFR (2021)
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. CoRR (2021)
Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
On the Effectiveness of Iterative Learning Control. CoRR (2021)
Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Exploration in Action Space. CoRR (2020)
Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. CoRR (2019)
Wen Sun, Anirudh Vemula, Byron Boots, J. Andrew Bagnell
Provably Efficient Imitation Learning from Observation Alone. CoRR (2019)
Liyiming Ke, Matt Barnes, Wen Sun, Gilwoo Lee, Sanjiban Choudhury, Siddhartha S. Srinivasa
Imitation Learning as f-Divergence Minimization. CoRR (2019)
Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell
Provably Efficient Imitation Learning from Observation Alone. ICML (2019)
Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. AISTATS (2019)
Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha S. Srinivasa, Geoffrey J. Gordon
Recurrent Predictive State Policy Networks. ICML (2018)
Wen Sun, J. Andrew Bagnell, Byron Boots
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. CoRR (2018)
Wen Sun, J. Andrew Bagnell, Byron Boots
Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. ICLR (Poster) (2018)
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
Dual Policy Iteration. CoRR (2018)
Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha S. Srinivasa, Geoffrey J. Gordon
Recurrent Predictive State Policy Networks. CoRR (2018)
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
Dual Policy Iteration. NeurIPS (2018)
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell
Predictive-State Decoders: Encoding the Future into Recurrent Networks. CoRR (2017)
Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
Gradient Boosting on Stochastic Data Streams. CoRR (2017)
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. ICML (2017)
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell
Predictive-State Decoders: Encoding the Future into Recurrent Networks. NIPS (2017)
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. CoRR (2017)
Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
Gradient Boosting on Stochastic Data Streams. AISTATS (2017)
Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots
Learning to Smooth with Bidirectional Predictive State Inference Machines. UAI (2016)
Wen Sun, J. Andrew Bagnell
Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees. IJCAI (2016)
Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell
Inference Machines for Nonparametric Filter Learning. IJCAI (2016)
Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell
Learning to Filter with Predictive State Inference Machines. ICML (2016)
Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots
Online Instrumental Variable Regression with Applications to Online Linear System Identification. AAAI (2016)
Wen Sun, Jur van den Berg, Ron Alterovitz
Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty. IEEE Trans Autom. Sci. Eng. 13 (2) (2016)
Wen Sun, Sachin Patil, Ron Alterovitz
High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning. IEEE Trans. Robotics 31 (1) (2015)
Wen Sun, J. Andrew Bagnell
Online Bellman Residual Algorithms with Predictive Error Guarantees. UAI (2015)
Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell
Learning to Filter with Predictive State Inference Machines. CoRR (2015)
Wen Sun, Islam S. M. Khalil, Sarthak Misra, Ron Alterovitz
Motion planning for paramagnetic microparticles under motion and sensing uncertainty. ICRA (2014)
Wen Sun, Jur van den Berg, Ron Alterovitz
Stochastic Extended LQR: Optimization-Based Motion Planning Under Uncertainty. WAFR (2014)
Wen Sun, Ron Alterovitz
Motion planning under uncertainty for medical needle steering using optimization in belief space. IROS (2014)
Wen Sun, Luis G. Torres, Jur van den Berg, Ron Alterovitz
Safe Motion Planning for Imprecise Robotic Manipulators by Minimizing Probability of Collision. ISRR (2013)