Mengdi Wang

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Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAVerified email at ee.ucla.edu
Csaba SzepesvariDeepMind & University of AlbertaVerified email at cs.ualberta.ca
Yinyu YeK.T. Li Professor of Engineering, Stanford UniversityVerified email at stanford.edu
Dimitri BertsekasArizona State University - Massachusetts Institute of TechnologyVerified email at mit.edu
Alec KoppelAI Research Lead, JP Morgan AI ResearchVerified email at jpmchase.com
Ethan X. FangAssociate Professor at Duke UniversityVerified email at duke.edu
Aaron SidfordStanford UniversityVerified email at stanford.edu
Tuo ZhaoAssistant Professor, Georgia TechVerified email at gatech.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Botao HaoDeepmindVerified email at google.com
Tong ZhangHKUSTVerified email at tongzhang-ml.org
Zhaoran WangAssistant Professor at Northwestern UniversityVerified email at northwestern.edu
Anru ZhangDuke UniversityVerified email at duke.edu
Saeed GhadimiUniversity of WaterlooVerified email at uwaterloo.ca
Tor LattimoreDeepMindVerified email at google.com
Yu-Xiang WangAssistant Professor of Computer Science, UC Santa BarbaraVerified email at cs.ucsb.edu
Andrzej RuszczyńskiBoard of Governors Professor of Rutgers UniversityVerified email at business.rutgers.edu
Vladimir BravermanVictor E. Cameron Professor of Computer Science, Rice UniversityVerified email at rice.edu
Zheng Tracy KeHarvard UniversityVerified email at fas.harvard.edu
Lihong Li (李力鸿)AmazonVerified email at amazon.com

Mengdi Wang

Center for Statistics & Machine Learning, Electrical and Computer Engineering, Princeton

Verified email at princeton.edu - Homepage

reinforcement learning optimization machine learning data science control


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Sample-optimal parametric q-learning using linearly additive features L Yang, M Wang International Conference on Machine Learning, 6995-7004, 2019	248	2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound LF Yang, M Wang International Conference on Machine Learning, 2020, 2019	230	2019
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions M Wang, EX Fang, H Liu Mathematical Programming 161, 419-449, 2017	220	2017
Model-based reinforcement learning with value-targeted regression A Ayoub, Z Jia, C Szepesvari, M Wang, L Yang International Conference on Machine Learning, 463-474, 2020	215	2020
Near-optimal time and sample complexities for solving Markov decision processes with a generative model A Sidford, M Wang, X Wu, L Yang, Y Ye Advances in Neural Information Processing Systems 31, 2018	208*	2018
Approximation methods for bilevel programming S Ghadimi, M Wang arXiv preprint arXiv:1802.02246, 2018	133	2018
Accelerating stochastic composition optimization M Wang, J Liu, EX Fang Journal of Machine Learning Research, 2017, 2016	129	2016
Variance reduced value iteration and faster algorithms for solving markov decision processes A Sidford, M Wang, X Wu, Y Ye. Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2017	122	2017
Minimax-optimal off-policy evaluation with linear function approximation Y Duan, Z Jia, M Wang International Conference on Machine Learning, 2701-2709, 2020	106	2020
Stochastic first-order methods with random constraint projection M Wang, DP Bertsekas SIAM Journal on Optimization 26 (1), 681-717, 2016	103*	2016
Variational policy gradient method for reinforcement learning with general utilities J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang NeurIPS 2020, 2020	90	2020
A single timescale stochastic approximation method for nested stochastic optimization S Ghadimi, A Ruszczynski, M Wang SIAM Journal on Optimization 30 (1), 960-979, 2020	90	2020
Finite-sum composition optimization via variance reduced gradient descent X Lian, M Wang, J Liu Artificial Intelligence and Statistics. 2017., 2016	83	2016
A distributed tracking algorithm for reconstruction of graph signals X Wang, M Wang, Y Gu IEEE Journal of Selected Topics in Signal Processing 9 (4), 728-740, 2015	71	2015
On function approximation in reinforcement learning: Optimism in the face of large state spaces Z Yang, C Jin, Z Wang, M Wang, MI Jordan arXiv preprint arXiv:2011.04622, 2020	66*	2020
Randomized linear programming solves the Markov decision problem in nearly linear (sometimes sublinear) time M Wang Mathematics of Operations Research 45 (2), 517-546, 2020	66*	2020
Stochastic primal-dual methods and sample complexity of reinforcement learning Y Chen, M Wang arXiv preprint arXiv:1612.02516, 2016	66	2016
Primal-Dual Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems M Wang arXiv preprint arXiv:1710.06100, 2017	65	2017
Near-optimal stochastic approximation for online principal component estimation CJ Li, M Wang, H Liu, T Zhang Mathematical Programming 167, 75-97, 2018	63	2018
Solving discounted stochastic two-player games with near-optimal time and sample complexity A Sidford, M Wang, L Yang, Y Ye International Conference on Artificial Intelligence and Statistics, 2992-3002, 2020	62	2020

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