Wen Sun

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J. Andrew BagnellCarnegie Mellon UniversityDirección de correo verificada de ri.cmu.edu
Masatoshi UeharaGenentechDirección de correo verificada de gene.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityDirección de correo verificada de princeton.edu
Nathan KallusCornell UniversityDirección de correo verificada de cornell.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Byron BootsAssociate Professor, University of WashingtonDirección de correo verificada de cs.washington.edu
Alekh AgarwalGoogleDirección de correo verificada de google.com
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu
Yuda SongCarnegie Mellon UniversityDirección de correo verificada de andrew.cmu.edu
Kianté BrantleyAssistant Professor, Harvard UniversityDirección de correo verificada de g.harvard.edu
Nan JiangAssociate Professor of Computer Science, UIUCDirección de correo verificada de illinois.edu
Wenhao ZhanGraduate Student, Princeton UniversityDirección de correo verificada de princeton.edu
Xuezhou ZhangBoston UniversityDirección de correo verificada de bu.edu
Arun VenkatramanAurora Innovation / past: Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Ayush SekhariPostdoctoral Associate, MITDirección de correo verificada de mit.edu
Kaiwen WangComputer Science PhD at Cornell TechDirección de correo verificada de cornell.edu
Ron AlterovitzDistinguished Professor of Computer Science, University of North Carolina at Chapel HillDirección de correo verificada de cs.unc.edu
Ashish KapoorCEO and Chief Scientist, Scaled FoundationsDirección de correo verificada de scaledfoundations.ai
John LangfordMicrosoft Research New YorkDirección de correo verificada de hunch.net

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Wen Sun

Assistant Professor, Cornell University

Dirección de correo verificada de cornell.edu - Página principal

Machine Learning Motion Planning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	317	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	292	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	241	2021
Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021	185*	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	172	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
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	144	2020
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
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	125	2019
Disagreement-Regularized Imitation Learning K Brantley, W Sun, M Henaff International Conference on Learning Representations (ICLR 2020), 2020	120	2020
Provably Efficient Imitation Learning from Observation Alone W Sun, A Vemula, B Boots, JA Bagnell International Conference on Machine Learning, 2019	110	2019
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
Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning W Sun, JA Bagnell, B Boots International Conference on Learning Representations (ICLR 2018), 2018	105	2018
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	102	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	89	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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