Mazumdar, Eric

Combined from CaltechAUTHORS

• Maheshwari, Chinmay and Sasty, S. Shankar, et el. (2023) Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games
• Chen, Zaiwei and Zhang, Kaiqing, et el. (2023) A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
• Hardt, Moritz and Mazumdar, Eric, et el. (2023) Algorithmic Collective Action in Machine Learning
• Badithela, Apurva and Graebener, Josefine B., et el. (2022) Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows; 10.48550/arXiv.2210.10304
• Zrnic, Tijana and Mazumdar, Eric (2022) A Note on Zeroth-Order Optimization on the Simplex; 10.48550/arXiv.2208.01185
• Maheshwari, Chinmay and Mazumdar, Eric, et el. (2022) Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets; 10.48550/arXiv.arXiv.2206.02344
• Xu, Pan and Zheng, Hongkai, et el. (2022) Langevin Monte Carlo for Contextual Bandits; Proceedings of Machine Learning Research; Vol. 162; 24830-24850; 10.48550/arXiv.arXiv.2206.11254
• Chasnov, Benjamin and Ratliff, Lillian J., et el. (2021) Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings; 10.48550/arXiv.1906.00731
• Maheshwari, Chinmay and Chiu, Chih-Yuan, et el. (2021) Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization; 10.48550/arXiv.2106.09082
• Yu, Yaodong and Lin, Tianyi, et el. (2021) Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization; 10.48550/arXiv.2104.13326
• Dong, Roy and Mazumdar, Eric, et el. (2021) Optimal Causal Imputation for Control; 10.48550/arXiv.1703.07049
• Mazumdar, Eric and Pacchiano, Aldo, et el. (2021) On Thompson Sampling with Langevin Algorithms; 10.48550/arXiv.2002.10002
• Mazumdar, Eric and Ratliff, Lillian J., et el. (2021) Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games; 10.48550/arXiv.1907.03712
• Zrnic, Tijana and Mazumdar, Eric, et el. (2021) Who Leads and Who Follows in Strategic Classification?; 10.48550/arXiv.2106.12529
• Westenbroek, Tyler and Mazumdar, Eric, et el. (2020) Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning; ISBN 978-1-7281-7447-1; 2020 59th IEEE Conference on Decision and Control (CDC); 118-125; 10.1109/CDC42340.2020.9304242
• Mazumdar, Eric and Westenbroek, Tyler, et el. (2020) High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems; ISBN 978-1-7281-7447-1; 2020 59th IEEE Conference on Decision and Control (CDC); 4275-4280; 10.1109/CDC42340.2020.9304491
• Rubies-Royo, Vicenç and Mazumdar, Eric, et el. (2020) Expert Selection in High-Dimensional Markov Decision Processes; ISBN 978-1-7281-7447-1; 2020 59th IEEE Conference on Decision and Control (CDC); 3604-3610; 10.1109/CDC42340.2020.9303788
• Chasnov, Benjamin and Ratliff, Lillian, et el. (2020) Convergence Analysis of Gradient-Based Learning in Continuous Games; Proceedings of Machine Learning Research; Vol. 115; 935-944
• Westenbroek, Tyler and Fridovich-Keil, David, et el. (2020) Feedback Linearization for Uncertain Systems via Reinforcement Learning; ISBN 978-1-7281-7395-5; 2020 IEEE International Conference on Robotics and Automation (ICRA); 1364-1371; 10.1109/ICRA40945.2020.9197158
• Ratliff, Lillian J. and Mazumdar, Eric (2020) Inverse Risk-Sensitive Reinforcement Learning; IEEE Transactions on Automatic Control; Vol. 65; No. 3; 1256-1263; 10.1109/TAC.2019.2926674
• Mazumdar, Eric and Ratliff, Lillian J., et el. (2020) On Gradient-Based Learning in Continuous Games; SIAM Journal on Mathematics of Data Science; Vol. 2; No. 1; 103-131; 10.1137/18m1231298
• Mazumdar, Eric and Ratliff, Lillian J. (2019) Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games; ISBN 978-1-7281-1398-2; 2019 IEEE 58th Conference on Decision and Control (CDC); 6899-6904; 10.1109/CDC40024.2019.9030203
• Mazumdar, Eric and Jordan, Michael I., et el. (2019) On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games; 10.48550/arXiv.1901.00838
• Chapman, Margaret P. and Mazumdar, Eric V., et el. (2018) On the Analysis of Cyclic Drug Schedules for Cancer Treatment using Switched Dynamical Systems; ISBN 978-1-5386-1395-5; 2018 IEEE Conference on Decision and Control (CDC); 3503-3509; 10.1109/CDC.2018.8619490
• Mazumdar, Eric and Ratliff, Lillian J., et el. (2017) Gradient-based inverse risk-sensitive reinforcement learning; ISBN 978-1-5090-2873-3; 2017 IEEE 56th Annual Conference on Decision and Control (CDC); 5796-5801; 10.1109/CDC.2017.8264535
• Mazumdar, Eric and Dong, Roy, et el. (2017) A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes; 10.48550/arXiv.1707.05714
• Ratliff, Lillian J. and Dowling, Chase, et el. (2016) To observe or not to observe: Queuing game framework for urban parking; ISBN 978-1-5090-1837-6; 2016 IEEE 55th Conference on Decision and Control (CDC); 5286-5291; 10.1109/CDC.2016.7799079
• Calderone, Daniel and Mazumdar, Eric, et el. (2016) Understanding the impact of parking on urban mobility via routing games on queue-flow networks; 10.1109/CDC.2016.7799444