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Wen 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
— Yangtze University, School of Information and Mathematics, Jingzhou, China (and 1 more)
- Wen Sun 0004
- Wen Sun 0005 — University of Angers, France
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2020 – today
- 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 - [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
- [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 - [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
Provably Efficient CVaR RL in Low-rank MDPs. CoRR abs/2311.11965 (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
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
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 - [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
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
- [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 - [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
- [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 - [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)
2010 – 2019
- 2019
- [b1]Wen Sun:
Towards Generalization and Efficiency in Reinforcement Learning. Carnegie Mellon University, USA, 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 - [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
- [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 - [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
- [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 - [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) - 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) - [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
- [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) - [c5]Wen Sun, J. Andrew Bagnell:
Online Bellman Residual Algorithms with Predictive Error Guarantees. UAI 2015: 852-861 - [i1]Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell:
Learning to Filter with Predictive State Inference Machines. CoRR abs/1512.08836 (2015) - 2014
- [c4]Wen Sun, Islam S. M. Khalil
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
Coauthor Index
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