Wen Sun

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J. Andrew BagnellCarnegie Mellon UniversityVerified email at ri.cmu.edu
Byron BootsAssociate Professor, University of WashingtonVerified email at cs.washington.edu
Masatoshi UeharaCornell UniversityVerified email at cornell.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Alekh AgarwalGoogleVerified email at google.com
Arun VenkatramanAurora Innovation / past: Carnegie Mellon UniversityVerified email at cs.cmu.edu
Nan JiangAssistant Professor of Computer Science, UIUCVerified email at illinois.edu
Ron AlterovitzProfessor of Computer Science, University of North Carolina at Chapel HillVerified email at cs.unc.edu
Xuezhou ZhangPrinceton UniversityVerified email at princeton.edu
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Ashish KapoorGeneral Manager, Microsoft RedmondVerified email at microsoft.com
Nathan KallusCornell UniversityVerified email at cornell.edu
Yuda SongCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Anirudh VemulaGraduate Student, Robotics Institute, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
MAX SIMCHOWITZMITVerified email at mit.edu
Siddhartha SrinivasaBoeing Endowed Professor, University of WashingtonVerified email at cs.washington.edu
martial hebertCMUVerified email at ri.cmu.edu

Wen Sun

Assistant Professor, Cornell University

Verified email at cornell.edu - Homepage

Machine Learning Motion Planning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction W Sun, A Venkatraman, GJ Gordon, B Boots, JA Bagnell International Conference on Machine Learning, 2017	223	2017
Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches W Sun, N Jiang, A Krishnamurthy, A Agarwal, J Langford Conference of Learning Theory (COLT 2019), 2019	165*	2019
Flambe: Structural complexity and representation learning of low rank mdps A Agarwal, S Kakade, A Krishnamurthy, W Sun Advances in neural information processing systems 33, 20095-20107, 2020	131	2020
Reinforcement learning: Theory and algorithms A Agarwal, N Jiang, SM Kakade, W Sun CS Dept., UW Seattle, Seattle, WA, USA, Tech. Rep, 10-4, 2019	117	2019
Bilinear classes: A structural framework for provable generalization in rl S Du, S Kakade, J Lee, S Lovett, G Mahajan, W Sun, R Wang International Conference on Machine Learning, 2826-2836, 2021	103	2021
Imitation learning as f-divergence minimization L Ke, S Choudhury, M Barnes, W Sun, G Lee, S Srinivasa Algorithmic Foundations of Robotics XIV: Proceedings of the Fourteenth …, 2021	98	2021
Pc-pg: Policy cover directed exploration for provable policy gradient learning A Agarwal, M Henaff, S Kakade, W Sun Advances in neural information processing systems 33, 13399-13412, 2020	92	2020
Corruption-robust exploration in episodic reinforcement learning T Lykouris, M Simchowitz, A Slivkins, W Sun Conference on Learning Theory, 3242-3245, 2021	83	2021
Information theoretic regret bounds for online nonlinear control S Kakade, A Krishnamurthy, K Lowrey, M Ohnishi, W Sun Advances in Neural Information Processing Systems 33, 15312-15325, 2020	83	2020
High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning W Sun, S Patil, R Alterovitz IEEE Transactions on Robotics 31 (1), 104 - 116, 2015	81	2015
Policy poisoning in batch reinforcement learning and control Y Ma, X Zhang, W Sun, J Zhu Advances in Neural Information Processing Systems 32, 2019	79	2019
Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning W Sun, JA Bagnell, B Boots International Conference on Learning Representations (ICLR 2018), 2018	79	2018
Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty W Sun, J van den Berg, R Alterovitz IEEE Transactions on Automation Science and Engineering 13 (2), 437-447, 2016	78	2016
Provably Efficient Imitation Learning from Observation Alone W Sun, A Vemula, B Boots, JA Bagnell International Conference on Machine Learning, 2019	74	2019
Disagreement-Regularized Imitation Learning K Brantley, W Sun, M Henaff International Conference on Learning Representations (ICLR 2020), 2020	71	2020
Dual Policy Iteration W Sun, G Gordon, JA Bagnell, B Boots Advances in Neural Information Processing Systems, 2018	65	2018
Safety-aware algorithms for adversarial contextual bandit W Sun, D Dey, A Kapoor International Conference on Machine Learning, 3280-3288, 2017	57	2017
Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021	56*	2021
Learning to filter with predictive state inference machines W Sun, A Venkatraman, B Boots, JA Bagnell International Conference on Machine Learning, 1197-1205, 2016	55	2016
Representation learning for online and offline rl in low-rank mdps M Uehara, X Zhang, W Sun arXiv preprint arXiv:2110.04652, 2021	47	2021

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