Wen Sun

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J. Andrew BagnellCarnegie Mellon UniversityGeverifieerd e-mailadres voor ri.cmu.edu
Masatoshi UeharaCornell 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
Arun VenkatramanAurora Innovation / past: Carnegie Mellon UniversityGeverifieerd e-mailadres voor cs.cmu.edu
Nan JiangAssistant Professor of Computer Science, UIUCGeverifieerd e-mailadres voor illinois.edu
Xuezhou ZhangBoston UniversityGeverifieerd e-mailadres voor bu.edu
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityGeverifieerd e-mailadres voor cs.cmu.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityGeverifieerd e-mailadres voor princeton.edu
Ron AlterovitzProfessor of Computer Science, University of North Carolina at Chapel HillGeverifieerd e-mailadres voor cs.unc.edu
Nathan KallusCornell UniversityGeverifieerd e-mailadres voor cornell.edu
Ashish KapoorCEO and Chief Scientist, Scaled FoundationsGeverifieerd e-mailadres voor scaledfoundations.ai
Yuda SongCarnegie Mellon UniversityGeverifieerd e-mailadres voor andrew.cmu.edu
John LangfordMicrosoft Research New YorkGeverifieerd e-mailadres voor hunch.net
Anirudh VemulaAurora InnovationGeverifieerd e-mailadres voor aurora.tech
MAX SIMCHOWITZMITGeverifieerd e-mailadres voor mit.edu
Siddhartha SrinivasaBoeing Endowed Professor, University of WashingtonGeverifieerd e-mailadres voor cs.washington.edu
Zhao SongAdobe ResearchGeverifieerd e-mailadres voor ias.edu

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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
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction W Sun, A Venkatraman, GJ Gordon, B Boots, JA Bagnell International Conference on Machine Learning, 2017	246	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	200	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	183	2020
Reinforcement learning: Theory and algorithms A Agarwal, N Jiang, SM Kakade, W Sun CS Dept., UW Seattle, Seattle, WA, USA, Tech. Rep 32, 2019	162	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	149	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	113	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	107	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	97	2020
Corruption-robust exploration in episodic reinforcement learning T Lykouris, M Simchowitz, A Slivkins, W Sun Conference on Learning Theory, 3242-3245, 2021	95	2021
Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning W Sun, JA Bagnell, B Boots International Conference on Learning Representations (ICLR 2018), 2018	90	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	90	2015
Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021	88*	2021
Disagreement-Regularized Imitation Learning K Brantley, W Sun, M Henaff International Conference on Learning Representations (ICLR 2020), 2020	86	2020
Provably Efficient Imitation Learning from Observation Alone W Sun, A Vemula, B Boots, JA Bagnell International Conference on Machine Learning, 2019	86	2019
Policy poisoning in batch reinforcement learning and control Y Ma, X Zhang, W Sun, J Zhu Advances in Neural Information Processing Systems 32, 2019	86	2019
Representation learning for online and offline rl in low-rank mdps M Uehara, X Zhang, W Sun arXiv preprint arXiv:2110.04652, 2021	83	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	82	2016
Dual Policy Iteration W Sun, G Gordon, JA Bagnell, B Boots Advances in Neural Information Processing Systems, 2018	72	2018
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	64	2020
Safety-aware algorithms for adversarial contextual bandit W Sun, D Dey, A Kapoor International Conference on Machine Learning, 3280-3288, 2017	62	2017

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