Mengdi Wang
Mengdi Wang
Center for Statistics & Machine Learning, Electrical and Computer Engineering, Princeton
Verified email at - Homepage
Cited by
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Sample-optimal parametric q-learning using linearly additive features
L Yang, M Wang
International Conference on Machine Learning, 6995-7004, 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
LF Yang, M Wang
International Conference on Machine Learning, 2020, 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
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
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
Approximation methods for bilevel programming
S Ghadimi, M Wang
arXiv preprint arXiv:1802.02246, 2018
Accelerating stochastic composition optimization
M Wang, J Liu, EX Fang
Journal of Machine Learning Research, 2017, 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
Minimax-optimal off-policy evaluation with linear function approximation
Y Duan, Z Jia, M Wang
International Conference on Machine Learning, 2701-2709, 2020
Stochastic first-order methods with random constraint projection
M Wang, DP Bertsekas
SIAM Journal on Optimization 26 (1), 681-717, 2016
Variational policy gradient method for reinforcement learning with general utilities
J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang
NeurIPS 2020, 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
Finite-sum composition optimization via variance reduced gradient descent
X Lian, M Wang, J Liu
Artificial Intelligence and Statistics. 2017., 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
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
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
Stochastic primal-dual methods and sample complexity of reinforcement learning
Y Chen, M Wang
arXiv preprint arXiv:1612.02516, 2016
Primal-Dual Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems
M Wang
arXiv preprint arXiv:1710.06100, 2017
Near-optimal stochastic approximation for online principal component estimation
CJ Li, M Wang, H Liu, T Zhang
Mathematical Programming 167, 75-97, 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
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