Mengdi Wang
Mengdi Wang
Associate Professor at Princeton University and DeepMind
Verified email at princeton.edu - Homepage
Title
Cited by
Cited by
Year
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
M Wang, EX Fang, H Liu
Mathematical Programming 161 (1-2), 419-449, 2017
1372017
Near-optimal time and sample complexities for solving Markov decision processes with a generative model
A Sidford, M Wang, X Wu, LF Yang, Y Ye
Proceedings of the 32nd International Conference on Neural Information …, 2018
108*2018
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
LF Yang, M Wang
International Conference on Machine Learning, 2020, 2019
1052019
Sample-optimal parametric Q-learning using linearly additive features
L Yang, M Wang
International Conference on Machine Learning, 6995-7004, 2019
102*2019
Stochastic first-order methods with random constraint projection
M Wang, DP Bertsekas
SIAM Journal on Optimization 26 (1), 681-717, 2016
90*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
862017
Accelerating stochastic composition optimization
M Wang, J Liu, EX Fang
Journal of Machine Learning Research, 2017, 2016
812016
Finite-sum composition optimization via variance reduced gradient descent
X Lian, M Wang, J Liu
Artificial Intelligence and Statistics. 2017., 2016
592016
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
562020
Near-optimal stochastic approximation for online principal component estimation
CJ Li, M Wang, H Liu, T Zhang
Mathematical Programming 167 (1), 75-97, 2018
562018
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
532015
Stochastic primal-dual methods and sample complexity of reinforcement learning
Y Chen, M Wang
arXiv preprint arXiv:1612.02516, 2016
462016
Incremental constraint projection methods for variational inequalities
M Wang, DP Bertsekas
Mathematical Programming 150 (2), 321-363, 2015
432015
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
38*2020
Minimax-optimal off-policy evaluation with linear function approximation
Y Duan, Z Jia, M Wang
International Conference on Machine Learning, 2701-2709, 2020
362020
Scalable Bilinear Learning Using State and Action Features
Y Chen, L Li, M Wang
International Conference on Machine Learning, 2018
362018
Primal-Dual Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems
M Wang
arXiv preprint arXiv:1710.06100, 2017
342017
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
332020
Multilevel stochastic gradient methods for nested composition optimization
S Yang, M Wang, EX Fang
SIAM Journal on Optimization 29 (1), 616-659, 2019
262019
Variance reduction methods for sublinear reinforcement learning
S Kakade, M Wang, LF Yang
arXiv preprint arXiv:1802.09184, 2018
252018
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