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Eric Mazumdar
Eric Mazumdar
Geverifieerd e-mailadres voor caltech.edu - Homepage
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On gradient-based learning in continuous games
E Mazumdar, LJ Ratliff, SS Sastry
SIAM Journal on Mathematics of Data Science 2 (1), 103-131, 2020
214*2020
On finding local nash equilibria (and only local nash equilibria) in zero-sum games
EV Mazumdar, MI Jordan, SS Sastry
arXiv preprint arXiv:1901.00838, 2019
1592019
Feedback linearization for uncertain systems via reinforcement learning
T Westenbroek, D Fridovich-Keil, E Mazumdar, S Arora, V Prabhu, ...
2020 IEEE International Conference on Robotics and Automation (ICRA), 1364-1371, 2020
72*2020
Who leads and who follows in strategic classification?
T Zrnic, E Mazumdar, S Sastry, M Jordan
Advances in Neural Information Processing Systems 34, 15257-15269, 2021
652021
Mathematical framework for activity-based cancer biomarkers
GA Kwong, JS Dudani, E Carrodeguas, EV Mazumdar, SM Zekavat, ...
Proceedings of the National Academy of Sciences 112 (41), 12627-12632, 2015
602015
On approximate Thompson sampling with Langevin algorithms
E Mazumdar, A Pacchiano, Y Ma, M Jordan, P Bartlett
International Conference on Machine Learning, 6797-6807, 2020
58*2020
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games
E Mazumdar, LJ Ratliff, MI Jordan, SS Sastry
arXiv preprint arXiv:1907.03712, 2019
53*2019
Gradient-based inverse risk-sensitive reinforcement learning
E Mazumdar, LJ Ratliff, T Fiez, SS Sastry
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5796-5801, 2017
50*2017
Convergence Guarantees for Gradient-Based Learning in Continuous Games.
B Chasnov, LJ Ratliff, E Mazumdar, S Burden
Uncertainty in artificial intelligence, 2019
49*2019
Langevin monte carlo for contextual bandits
P Xu, H Zheng, EV Mazumdar, K Azizzadenesheli, A Anandkumar
International Conference on Machine Learning, 24830-24850, 2022
372022
Fast distributionally robust learning with variance-reduced min-max optimization
Y Yu, T Lin, EV Mazumdar, M Jordan
International Conference on Artificial Intelligence and Statistics, 1219-1250, 2022
342022
Global convergence to local minmax equilibrium in classes of nonconvex zero-sum games
T Fiez, L Ratliff, E Mazumdar, E Faulkner, A Narang
Advances in Neural Information Processing Systems 34, 29049-29063, 2021
342021
Algorithmic collective action in machine learning
M Hardt, E Mazumdar, C Mendler-Dünner, T Zrnic
International Conference on Machine Learning, 12570-12586, 2023
232023
Zeroth-order methods for convex-concave min-max problems: Applications to decision-dependent risk minimization
C Maheshwari, CY Chiu, E Mazumdar, S Sastry, L Ratliff
International Conference on Artificial Intelligence and Statistics, 6702-6734, 2022
212022
To observe or not to observe: Queuing game framework for urban parking
LJ Ratliff, C Dowling, E Mazumdar, B Zhang
2016 IEEE 55th Conference on Decision and Control (CDC), 5286-5291, 2016
182016
Decentralized, communication-and coordination-free learning in structured matching markets
C Maheshwari, S Sastry, E Mazumdar
Advances in Neural Information Processing Systems 35, 15081-15092, 2022
162022
Understanding the impact of parking on urban mobility via routing games on queue-flow networks
D Calderone, E Mazumdar, LJ Ratliff, SS Sastry
2016 IEEE 55th Conference on Decision and Control (CDC), 7605-7610, 2016
142016
A finite-sample analysis of payoff-based independent learning in zero-sum stochastic games
Z Chen, K Zhang, E Mazumdar, A Ozdaglar, A Wierman
Advances in Neural Information Processing Systems 36, 2024
132024
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games
E Mazumdar, L Ratliff
arXiv preprint arXiv:2002.01007, 2020
122020
Convergent first-order methods for bi-level optimization and stackelberg games
C Maheshwari, SS Sasty, L Ratliff, E Mazumdar
arXiv preprint arXiv:2302.01421, 2023
11*2023
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Artikelen 1–20