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ORCIDWen Sun 0002
Person information
- affiliation: Cornell University, Ithaca, NY, USA
- affiliation: Carnegie Mellon University, Robotics Institute, Pittsburgh, PA, USA
- affiliation (former): University of North Carolina at Chapel Hill, Department of Computer Science, NC, USA
Other persons with the same name
- Wen Sun — disambiguation page
- Wen Sun 0001 — University of Science and Technology of China, Hefei, China
- Wen Sun 0003
![[0000-0001-7070-4730]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-7070-4730")
— Yangtze University, School of Information and Mathematics, Jingzhou, China (and 1 more) - Wen Sun 0004 — Xidian University, School of Cyber Engineering, State Key Laboratory of Integrated Services Networks, Xi'an, China (and 1 more)
- Wen Sun 0005 — University of Angers, France
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Books and Theses
- 2019
[b1]Wen Sun:
Towards Generalization and Efficiency in Reinforcement Learning. Carnegie Mellon University, USA, 2019
Journal Articles
- 2016
- [j2]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): 437-447 (2016) - 2015
- [j1]Wen Sun, Sachin Patil, Ron Alterovitz:
High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning. IEEE Trans. Robotics 31(1): 104-116 (2015)
Conference and Workshop Papers
- 2024
- [c65]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 - [c64]Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun:
Adversarial Imitation Learning via Boosting. ICLR 2024 - [c63]Runzhe Wu, Wen Sun:
Making RL with Preference-based Feedback Efficient via Randomization. ICLR 2024 - [c62]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
Provable Reward-Agnostic Preference-Based Reinforcement Learning. ICLR 2024 - [c61]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Preference-Based Reinforcement Learning. ICLR 2024 - [c60]Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun:
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. ICLR 2024 - [c59]Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun:
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. ICML 2024 - [c58]Junxiong Wang, Kaiwen Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun:
JoinGym: An Efficient Join Order Selection Environment. RLC 2024: 64-91 - [c57]Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley, Wen Sun:
RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference. RLC 2024: 1656-1673 - 2023
- [c56]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. COLT 2023: 2114-2187 - [c55]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 - [c54]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. ICLR 2023 - [c53]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: 34615-34641 - [c52]Kaiwen Wang, Nathan Kallus, Wen Sun:
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. ICML 2023: 35864-35907 - [c51]Runzhe Wu, Masatoshi Uehara, Wen Sun:
Distributional Offline Policy Evaluation with Predictive Error Guarantees. ICML 2023: 37685-37712 - [c50]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning with Preference-Based Active Queries. NeurIPS 2023 - [c49]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Selective Sampling and Imitation Learning via Online Regression. NeurIPS 2023 - [c48]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 - [c47]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS 2023 - [c46]Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun:
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. NeurIPS 2023 - 2022
- [c45]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Corruption-robust Offline Reinforcement Learning. AISTATS 2022: 5757-5773 - [c44]Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris M. Kitani:
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. ICLR 2022 - [c43]Masatoshi Uehara, Wen Sun:
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. ICLR 2022 - [c42]Masatoshi Uehara, Xuezhou Zhang, Wen Sun:
Representation Learning for Online and Offline RL in Low-rank MDPs. ICLR 2022 - [c41]Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. ICML 2022: 2938-2971 - [c40]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: 26517-26547 - [c39]Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell:
On the Effectiveness of Iterative Learning Control. L4DC 2022: 47-58 - [c38]Yuda Song, Ye Yuan, Wen Sun, Kris Kitani:
Online No-regret Model-Based Meta RL for Personalized Navigation. L4DC 2022: 166-179 - [c37]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - 2021
- [c36]Thodoris Lykouris, Max Simchowitz, Alex Slivkins, Wen Sun:
Corruption-robust exploration in episodic reinforcement learning. COLT 2021: 3242-3245 - [c35]Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang:
Bilinear Classes: A Structural Framework for Provable Generalization in RL. ICML 2021: 2826-2836 - [c34]Yuda Song, Wen Sun:
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration. ICML 2021: 9801-9811 - [c33]Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims:
Fairness of Exposure in Stochastic Bandits. ICML 2021: 10686-10696 - [c32]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Robust Policy Gradient against Strong Data Corruption. ICML 2021: 12391-12401 - [c31]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: 965-979 - [c30]Rahul Kidambi, Jonathan D. Chang, Wen Sun:
MobILE: Model-Based Imitation Learning From Observation Alone. NeurIPS 2021: 28598-28611 - [c29]Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha S. Srinivasa:
Imitation Learning as f-Divergence Minimization. WAFR 2021: 313-329 - 2020
- [c28]Kianté Brantley, Wen Sun, Mikael Henaff:
Disagreement-Regularized Imitation Learning. ICLR 2020 - [c27]Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao:
Provably Efficient Model-based Policy Adaptation. ICML 2020: 9088-9098 - [c26]Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. NeurIPS 2020 - [c25]Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. NeurIPS 2020 - [c24]Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. NeurIPS 2020 - [c23]Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun:
Information Theoretic Regret Bounds for Online Nonlinear Control. NeurIPS 2020 - [c22]Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS 2020 - 2019
- [c21]Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. AISTATS 2019: 2926-2935 - [c20]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT 2019: 2898-2933 - [c19]Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. ICML 2019: 6026-6035 - [c18]Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell:
Provably Efficient Imitation Learning from Observation Alone. ICML 2019: 6036-6045 - [c17]Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu:
Policy Poisoning in Batch Reinforcement Learning and Control. NeurIPS 2019: 14543-14553 - 2018
- [c16]Wen Sun, J. Andrew Bagnell, Byron Boots:
Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. ICLR (Poster) 2018 - [c15]Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha S. Srinivasa, Geoffrey J. Gordon:
Recurrent Predictive State Policy Networks. ICML 2018: 1954-1963 - [c14]Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Dual Policy Iteration. NeurIPS 2018: 7059-7069 - 2017
- [c13]Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell:
Gradient Boosting on Stochastic Data Streams. AISTATS 2017: 595-603 - [c12]Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. ICML 2017: 3309-3318 - [c11]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: 1172-1183 - 2016
- [c10]Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots:
Online Instrumental Variable Regression with Applications to Online Linear System Identification. AAAI 2016: 2101-2107 - [c9]Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell:
Learning to Filter with Predictive State Inference Machines. ICML 2016: 1197-1205 - [c8]Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell:
Inference Machines for Nonparametric Filter Learning. IJCAI 2016: 2074-2081 - [c7]Wen Sun, J. Andrew Bagnell:
Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees. IJCAI 2016: 4213-4217 - [c6]Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots:
Learning to Smooth with Bidirectional Predictive State Inference Machines. UAI 2016 - 2015
- [c5]Wen Sun, J. Andrew Bagnell:
Online Bellman Residual Algorithms with Predictive Error Guarantees. UAI 2015: 852-861 - 2014
- [c4]Wen Sun, Islam S. M. Khalil, Sarthak Misra, Ron Alterovitz:
Motion planning for paramagnetic microparticles under motion and sensing uncertainty. ICRA 2014: 5811-5817 - [c3]Wen Sun, Ron Alterovitz:
Motion planning under uncertainty for medical needle steering using optimization in belief space. IROS 2014: 1775-1781 - [c2]Wen Sun, Jur van den Berg, Ron Alterovitz:
Stochastic Extended LQR: Optimization-Based Motion Planning Under Uncertainty. WAFR 2014: 609-626 - 2013
- [c1]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: 685-701
Informal and Other Publications
- 2024
- [i67]Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun:
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. CoRR abs/2402.07198 (2024) - [i66]Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun:
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. CoRR abs/2403.06323 (2024) - [i65]Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang:
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. CoRR abs/2404.00099 (2024) - [i64]Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley, Wen Sun:
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation. CoRR abs/2404.03673 (2024) - [i63]Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun:
Dataset Reset Policy Optimization for RLHF. CoRR abs/2404.08495 (2024) - [i62]Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun:
Adversarial Imitation Learning via Boosting. CoRR abs/2404.08513 (2024) - [i61]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 abs/2404.16767 (2024) - [i60]Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun:
Understanding Preference Fine-Tuning Through the Lens of Coverage. CoRR abs/2406.01462 (2024) - [i59]Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun:
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. CoRR abs/2406.11810 (2024) - [i58]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 abs/2407.13399 (2024) - [i57]Kaiwen Wang, Nathan Kallus, Wen Sun:
The Central Role of the Loss Function in Reinforcement Learning. CoRR abs/2409.12799 (2024) - [i56]Zhaolin Gao, Wenhao Zhan, Jonathan D. Chang, Gokul Swamy, Kianté Brantley, Jason D. Lee, Wen Sun:
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF. CoRR abs/2410.04612 (2024) - 2023
- [i55]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR abs/2302.02392 (2023) - [i54]Kaiwen Wang, Nathan Kallus, Wen Sun:
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. CoRR abs/2302.03201 (2023) - [i53]Runzhe Wu, Masatoshi Uehara, Wen Sun:
Distributional Offline Policy Evaluation with Predictive Error Guarantees. CoRR abs/2302.09456 (2023) - [i52]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Reinforcement Learning with Human Feedback. CoRR abs/2305.14816 (2023) - [i51]Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun:
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. CoRR abs/2305.15703 (2023) - [i50]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
How to Query Human Feedback Efficiently in RL? CoRR abs/2305.18505 (2023) - [i49]Jonathan D. Chang, Kianté Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun:
Learning to Generate Better Than Your LLM. CoRR abs/2306.11816 (2023) - [i48]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Selective Sampling and Imitation Learning via Online Regression. CoRR abs/2307.04998 (2023) - [i47]Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun:
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. CoRR abs/2307.11704 (2023) - [i46]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning via Preference-Based Active Queries. CoRR abs/2307.12926 (2023) - [i45]Runzhe Wu, Wen Sun:
Making RL with Preference-based Feedback Efficient via Randomization. CoRR abs/2310.14554 (2023) - [i44]Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun:
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. CoRR abs/2311.08384 (2023) - [i43]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 abs/2311.11965 (2023) - 2022
- [i42]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach. CoRR abs/2202.00063 (2022) - [i41]Yuda Song, Ye Yuan, Wen Sun, Kris Kitani:
Online No-regret Model-Based Meta RL for Personalized Navigation. CoRR abs/2204.01925 (2022) - [i40]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. CoRR abs/2205.14571 (2022) - [i39]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i38]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i37]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. CoRR abs/2207.05738 (2022) - [i36]Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. CoRR abs/2207.05837 (2022) - [i35]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 abs/2207.13081 (2022) - [i34]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 abs/2210.06718 (2022) - 2021
- [i33]Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie:
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR abs/2102.02981 (2021) - [i32]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Robust Policy Gradient against Strong Data Corruption. CoRR abs/2102.05800 (2021) - [i31]Rahul Kidambi, Jonathan D. Chang, Wen Sun:
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. CoRR abs/2102.10769 (2021) - [i30]Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims:
Fairness of Exposure in Stochastic Bandits. CoRR abs/2103.02735 (2021) - [i29]Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang:
Bilinear Classes: A Structural Framework for Provable Generalization in RL. CoRR abs/2103.10897 (2021) - [i28]Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. CoRR abs/2106.03207 (2021) - [i27]Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun:
Corruption-Robust Offline Reinforcement Learning. CoRR abs/2106.06630 (2021) - [i26]Masatoshi Uehara, Wen Sun:
Pessimistic Model-based Offline RL: PAC Bounds and Posterior Sampling under Partial Coverage. CoRR abs/2107.06226 (2021) - [i25]Yuda Song, Wen Sun:
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration. CoRR abs/2107.07410 (2021) - [i24]Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani:
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. CoRR abs/2110.03659 (2021) - [i23]Masatoshi Uehara, Xuezhou Zhang, Wen Sun:
Representation Learning for Online and Offline RL in Low-rank MDPs. CoRR abs/2110.04652 (2021) - [i22]Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell:
On the Effectiveness of Iterative Learning Control. CoRR abs/2111.09434 (2021) - 2020
- [i21]Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Exploration in Action Space. CoRR abs/2004.00500 (2020) - [i20]Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. CoRR abs/2006.05051 (2020) - [i19]Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao:
Provably Efficient Model-based Policy Adaptation. CoRR abs/2006.08051 (2020) - [i18]Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. CoRR abs/2006.10814 (2020) - [i17]Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun:
Information Theoretic Regret Bounds for Online Nonlinear Control. CoRR abs/2006.12466 (2020) - [i16]Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. CoRR abs/2007.08459 (2020) - [i15]Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR abs/2010.03799 (2020) - 2019
- [i14]Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. CoRR abs/1901.11503 (2019) - [i13]Wen Sun, Anirudh Vemula, Byron Boots, J. Andrew Bagnell:
Provably Efficient Imitation Learning from Observation Alone. CoRR abs/1905.10948 (2019) - [i12]Liyiming Ke, Matt Barnes, Wen Sun, Gilwoo Lee, Sanjiban Choudhury, Siddhartha S. Srinivasa:
Imitation Learning as f-Divergence Minimization. CoRR abs/1905.12888 (2019) - [i11]Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu:
Policy Poisoning in Batch Reinforcement Learning and Control. CoRR abs/1910.05821 (2019) - [i10]Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Corruption Robust Exploration in Episodic Reinforcement Learning. CoRR abs/1911.08689 (2019) - 2018
- [i9]Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha S. Srinivasa, Geoffrey J. Gordon:
Recurrent Predictive State Policy Networks. CoRR abs/1803.01489 (2018) - [i8]Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Dual Policy Iteration. CoRR abs/1805.10755 (2018) - [i7]Wen Sun, J. Andrew Bagnell, Byron Boots:
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. CoRR abs/1805.11240 (2018) - [i6]Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. CoRR abs/1807.06473 (2018) - [i5]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR abs/1811.08540 (2018) - 2017
- [i4]Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell:
Gradient Boosting on Stochastic Data Streams. CoRR abs/1703.00377 (2017) - [i3]Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. CoRR abs/1703.01030 (2017) - [i2]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 abs/1709.08520 (2017) - 2015
- [i1]Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell:
Learning to Filter with Predictive State Inference Machines. CoRR abs/1512.08836 (2015)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-14 22:06 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
