Wen Sun

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J. Andrew BagnellCarnegie Mellon UniversityGeverifieerd e-mailadres voor ri.cmu.edu
Masatoshi UeharaGenentechGeverifieerd e-mailadres voor gene.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityGeverifieerd e-mailadres voor princeton.edu
Nathan KallusCornell UniversityGeverifieerd e-mailadres voor cornell.edu
Byron BootsAssociate Professor, University of WashingtonGeverifieerd e-mailadres voor cs.washington.edu
Alekh AgarwalGoogleGeverifieerd e-mailadres voor google.com
Akshay KrishnamurthyUniversity of Massachusetts AmherstGeverifieerd e-mailadres voor cs.umass.edu
Sham M KakadeHarvard UniversityGeverifieerd e-mailadres voor seas.harvard.edu
Yuda SongCarnegie Mellon UniversityGeverifieerd e-mailadres voor andrew.cmu.edu
Kianté BrantleyAssistant Professor, Harvard UniversityGeverifieerd e-mailadres voor g.harvard.edu
Nan JiangAssociate Professor of Computer Science, UIUCGeverifieerd e-mailadres voor illinois.edu
Xuezhou ZhangBoston UniversityGeverifieerd e-mailadres voor bu.edu
Arun VenkatramanAurora Innovation / past: Carnegie Mellon UniversityGeverifieerd e-mailadres voor cs.cmu.edu
Wenhao ZhanGraduate Student, Princeton UniversityGeverifieerd e-mailadres voor princeton.edu
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityGeverifieerd e-mailadres voor cs.cmu.edu
Ayush SekhariPostdoctoral Associate, MITGeverifieerd e-mailadres voor mit.edu
Ron AlterovitzDistinguished Professor of Computer Science, University of North Carolina at Chapel HillGeverifieerd e-mailadres voor cs.unc.edu
Kaiwen WangComputer Science PhD at Cornell TechGeverifieerd e-mailadres voor cornell.edu
Ashish KapoorCEO and Chief Scientist, Scaled FoundationsGeverifieerd e-mailadres voor scaledfoundations.ai
John LangfordMicrosoft Research New YorkGeverifieerd e-mailadres voor hunch.net

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Wen Sun

Assistant Professor, Cornell University

Geverifieerd e-mailadres voor cornell.edu - Homepage

Machine Learning Motion Planning


Titel Sorteren op citaties Sorteren op jaar Sorteren op titel	Geciteerd door Geciteerd door	Jaar
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	299	2019
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction W Sun, A Venkatraman, GJ Gordon, B Boots, JA Bagnell International Conference on Machine Learning, 2017	280	2017
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	275	2020
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	269	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	230	2021
Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021	172*	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	164	2021
Representation learning for online and offline rl in low-rank mdps M Uehara, X Zhang, W Sun arXiv preprint arXiv:2110.04652, 2021	142	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	139	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	138	2020
Corruption-robust exploration in episodic reinforcement learning T Lykouris, M Simchowitz, A Slivkins, W Sun Conference on Learning Theory, 3242-3245, 2021	124	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	121	2019
Disagreement-Regularized Imitation Learning K Brantley, W Sun, M Henaff International Conference on Learning Representations (ICLR 2020), 2020	117	2020
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
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	105	2015
Provably Efficient Imitation Learning from Observation Alone W Sun, A Vemula, B Boots, JA Bagnell International Conference on Machine Learning, 2019	101	2019
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	92	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	87	2020
Safety-aware algorithms for adversarial contextual bandit W Sun, D Dey, A Kapoor International Conference on Machine Learning, 3280-3288, 2017	79	2017

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