Mengdi Wang

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Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAAdresse e-mail validée de ee.ucla.edu
Csaba SzepesvariDeepMind & University of AlbertaAdresse e-mail validée de cs.ualberta.ca
Dimitri BertsekasArizona State University - Massachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Yinyu YeK.T. Li Professor of Engineering, Stanford UniversityAdresse e-mail validée de stanford.edu
Yaqi DuanPrinceton UniversityAdresse e-mail validée de mit.edu
Han LiuAssociate Professor, Department of Computer Science, Northwestern UniversityAdresse e-mail validée de princeton.edu
Ethan X. FangAssistant Professor at Duke UniversityAdresse e-mail validée de duke.edu
Junyu ZhangIndustrial Systems Engineering and Management, National University of SingaporeAdresse e-mail validée de nus.edu.sg
Alec KoppelResearch Scientist, AmazonAdresse e-mail validée de amazon.com
Aaron SidfordStanford UniversityAdresse e-mail validée de stanford.edu
Amrit Singh BediResearch Scientist at University of Maryland, College Park, MD, USA.Adresse e-mail validée de umd.edu
Ji LiuUniversity of RochesterAdresse e-mail validée de cs.rochester.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Tuo ZhaoAssistant Professor, Georgia TechAdresse e-mail validée de gatech.edu
Anru ZhangDuke UniversityAdresse e-mail validée de duke.edu
Botao HaoDeepmindAdresse e-mail validée de google.com
Zhaoran WangAssistant Professor at Northwestern UniversityAdresse e-mail validée de northwestern.edu
Tong ZhangHKUSTAdresse e-mail validée de tongzhang-ml.org
Yuantao GuDepartment of Electronic Engineering, Tsinghua UniversityAdresse e-mail validée de tsinghua.edu.cn
Saeed GhadimiUniversity of WaterlooAdresse e-mail validée de uwaterloo.ca

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Mengdi Wang

Associate Professor at Princeton University and DeepMind

Adresse e-mail validée de princeton.edu - Page d'accueil

reinforcement learning optimization machine learning data science control


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions M Wang, EX Fang, H Liu Mathematical Programming 161 (1), 419-449, 2017	178	2017
Sample-optimal parametric q-learning using linearly additive features L Yang, M Wang International Conference on Machine Learning, 6995-7004, 2019	168*	2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound LF Yang, M Wang International Conference on Machine Learning, 2020, 2019	162	2019
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	157*	2018
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	128	2020
Accelerating stochastic composition optimization M Wang, J Liu, EX Fang Journal of Machine Learning Research, 2017, 2016	105	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	102	2017
Stochastic first-order methods with random constraint projection M Wang, DP Bertsekas SIAM Journal on Optimization 26 (1), 681-717, 2016	94*	2016
Minimax-optimal off-policy evaluation with linear function approximation Y Duan, Z Jia, M Wang International Conference on Machine Learning, 2701-2709, 2020	69	2020
Approximation methods for bilevel programming S Ghadimi, M Wang arXiv preprint arXiv:1802.02246, 2018	67	2018
Finite-sum composition optimization via variance reduced gradient descent X Lian, M Wang, J Liu Artificial Intelligence and Statistics. 2017., 2016	66	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	59	2015
Near-optimal stochastic approximation for online principal component estimation CJ Li, M Wang, H Liu, T Zhang Mathematical Programming 167 (1), 75-97, 2018	58	2018
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	56	2020
Stochastic primal-dual methods and sample complexity of reinforcement learning Y Chen, M Wang arXiv preprint arXiv:1612.02516, 2016	56	2016
Incremental constraint projection methods for variational inequalities M Wang, DP Bertsekas Mathematical Programming 150 (2), 321-363, 2015	55	2015
Variational policy gradient method for reinforcement learning with general utilities J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang NeurIPS 2020, 2020	53	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	52*	2020
Primal-Dual Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems M Wang arXiv preprint arXiv:1710.06100, 2017	50	2017
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	45	2020

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