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
Tuo ZhaoAssociate Professor, Georgia TechVerified email at gatech.edu
Yinyu YeProfessor of Emeritus, Stanford UniversityVerified email at stanford.edu
Dimitri BertsekasArizona State University - Massachusetts Institute of TechnologyVerified email at mit.edu
Furong HuangAssociate Professor of Computer Science, University of MarylandVerified email at umd.edu
Dinesh ManochaDistinguished University Professor, University of Maryland at College ParkVerified email at umd.edu
Aaron SidfordStanford UniversityVerified email at stanford.edu
Prateek MittalProfessor, Princeton UniversityVerified email at princeton.edu
Botao HaoOpenAIVerified email at openai.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Tong ZhangUIUCVerified email at tongzhang-ml.org
Tor LattimoreDeepMindVerified email at google.com
Russ B AltmanProfessor Stanford UniversityVerified email at stanford.edu
Andrzej RuszczyńskiBoard of Governors Professor of Rutgers UniversityVerified email at business.rutgers.edu
Yu BaiOpenAIVerified email at openai.com
Denny ZhouResearch Scientist, Google DeepMindVerified email at google.com
Lihong Li (李力鸿)AmazonVerified email at amazon.com
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
John MulveyPrinceton UniversityVerified email at princeton.edu

Mengdi Wang

Center for Statistics & Machine Learning, ECE, Princeton University

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	388	2019
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	349	2020
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound LF Yang, M Wang International Conference on Machine Learning, 2020, 2019	342	2019
Approximation methods for bilevel programming S Ghadimi, M Wang arXiv preprint arXiv:1802.02246, 2018	309	2018
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions M Wang, EX Fang, H Liu Mathematical Programming 161, 419-449, 2017	296	2017
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	281	2018
Visual adversarial examples jailbreak aligned large language models X Qi, K Huang, A Panda, P Henderson, M Wang, P Mittal Proceedings of the AAAI conference on artificial intelligence 38 (19), 21527 …, 2024	249*	2024
Accelerating stochastic composition optimization M Wang, J Liu, EX Fang Journal of Machine Learning Research, 2017, 2016	172	2016
Minimax-optimal off-policy evaluation with linear function approximation Y Duan, Z Jia, M Wang International Conference on Machine Learning, 2701-2709, 2020	171	2020
Variational policy gradient method for reinforcement learning with general utilities J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang Advances in Neural Information Processing Systems 2020, 2020	169	2020
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	154*	2017
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	141	2020
Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data M Chen, K Huang, T Zhao, M Wang International Conference on Machine Learning, 2024	136	2024
Stochastic first-order methods with random constraint projection M Wang, DP Bertsekas SIAM Journal on Optimization 26 (1), 681-717, 2016	122*	2016
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	110*	2020
Primal-Dual Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems M Wang arXiv preprint arXiv:1710.06100, 2017	98*	2017
Finite-sum composition optimization via variance reduced gradient descent X Lian, M Wang, J Liu Artificial Intelligence and Statistics. 2017., 2016	98	2016
Assessing the brittleness of safety alignment via pruning and low-rank modifications B Wei, K Huang, Y Huang, T Xie, X Qi, M Xia, P Mittal, M Wang, ... ICML, 2024	90	2024
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	86	2020
Near-optimal offline reinforcement learning with linear representation: Leveraging variance information with pessimism M Yin, Y Duan, M Wang, YX Wang arXiv preprint arXiv:2203.05804, 2022	84	2022

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