Wen Sun

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J. Andrew BagnellCarnegie Mellon UniversityE-mail confirmado em ri.cmu.edu
Masatoshi UeharaGenentechE-mail confirmado em gene.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityE-mail confirmado em princeton.edu
Nathan KallusCornell UniversityE-mail confirmado em cornell.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstE-mail confirmado em cs.umass.edu
Byron BootsAssociate Professor, University of WashingtonE-mail confirmado em cs.washington.edu
Alekh AgarwalGoogleE-mail confirmado em google.com
Sham M KakadeHarvard UniversityE-mail confirmado em seas.harvard.edu
Yuda SongCarnegie Mellon UniversityE-mail confirmado em andrew.cmu.edu
Kianté BrantleyAssistant Professor, Harvard UniversityE-mail confirmado em g.harvard.edu
Nan JiangAssociate Professor of Computer Science, UIUCE-mail confirmado em illinois.edu
Wenhao ZhanGraduate Student, Princeton UniversityE-mail confirmado em princeton.edu
Xuezhou ZhangBoston UniversityE-mail confirmado em bu.edu
Arun VenkatramanAurora Innovation / past: Carnegie Mellon UniversityE-mail confirmado em cs.cmu.edu
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityE-mail confirmado em cs.cmu.edu
Ayush SekhariPostdoctoral Associate, MITE-mail confirmado em mit.edu
Kaiwen WangComputer Science PhD at Cornell TechE-mail confirmado em cornell.edu
Ron AlterovitzDistinguished Professor of Computer Science, University of North Carolina at Chapel HillE-mail confirmado em cs.unc.edu
Ashish KapoorCEO and Chief Scientist, Scaled FoundationsE-mail confirmado em scaledfoundations.ai
John LangfordMicrosoft Research New YorkE-mail confirmado em hunch.net

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Wen Sun

Assistant Professor, Cornell University

E-mail confirmado em cornell.edu - Página inicial

Machine Learning Motion Planning


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Reinforcement learning: Theory and algorithms A Agarwal, N Jiang, SM Kakade, W Sun CS Dept., UW Seattle, Seattle, WA, USA, Tech. Rep 32, 96, 2019	316	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	290	2020
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction W Sun, A Venkatraman, GJ Gordon, B Boots, JA Bagnell International Conference on Machine Learning, 2017	283	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	277	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	240	2021
Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021	184*	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	170	2021
Representation learning for online and offline rl in low-rank mdps M Uehara, X Zhang, W Sun arXiv preprint arXiv:2110.04652, 2021	155	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	142	2020
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	142	2020
Corruption-robust exploration in episodic reinforcement learning T Lykouris, M Simchowitz, A Slivkins, W Sun Conference on Learning Theory, 3242-3245, 2021	129	2021
Policy poisoning in batch reinforcement learning and control Y Ma, X Zhang, W Sun, J Zhu Advances in Neural Information Processing Systems 32, 2019	124	2019
Disagreement-Regularized Imitation Learning K Brantley, W Sun, M Henaff International Conference on Learning Representations (ICLR 2020), 2020	119	2020
Provably Efficient Imitation Learning from Observation Alone W Sun, A Vemula, B Boots, JA Bagnell International Conference on Machine Learning, 2019	109	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	107	2018
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	106	2015
Mitigating covariate shift in imitation learning via offline data with partial coverage J Chang, M Uehara, D Sreenivas, R Kidambi, W Sun Advances in Neural Information Processing Systems 34, 965-979, 2021	101	2021
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	92	2016
Multi-robot collision avoidance under uncertainty with probabilistic safety barrier certificates W Luo, W Sun, A Kapoor Advances in Neural Information Processing Systems 33, 372-383, 2020	88	2020
Hybrid rl: Using both offline and online data can make rl efficient Y Song, Y Zhou, A Sekhari, JA Bagnell, A Krishnamurthy, W Sun arXiv preprint arXiv:2210.06718, 2022	81	2022

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