Eric V. Mazumdar
Assistant Professor of Computing and Mathematical Sciences and Economics
Overview
Eric Mazumdar's research lies at the intersection of machine learning and economics. He is broadly interested in developing the tools and understanding necessary to confidently deploy machine learning algorithms into societal-scale systems. This requires understanding the theoretical underpinnings of learning algorithms in uncertain, dynamic environments where they must interact with other strategic agents, humans, and algorithms. Practically, he applies his work to work to problems in intelligent infrastructure, online markets, e-commerce, and the delivery of healthcare. Some of the topics addressed by his recent work include strategic classification, learning behavioral models of human decision-making from data, min-max optimization, learning in games, multi-agent reinforcement learning, distributionally robust learning, and learning for control.
Publications
- Chen, Zaiwei;Zhang, Kaiqing et al. (2023) A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
- Hardt, Moritz;Mazumdar, Eric et al. (2023) Algorithmic Collective Action in Machine Learning
- Maheshwari, Chinmay;Sasty, S. Shankar et al. (2023) Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games
- Badithela, Apurva;Graebener, Josefine B. et al. (2022) Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows
- Zrnic, Tijana;Mazumdar, Eric (2022) A Note on Zeroth-Order Optimization on the Simplex
- Xu, Pan;Zheng, Hongkai et al. (2022) Langevin Monte Carlo for Contextual BanditsProceedings of Machine Learning Research
- Maheshwari, Chinmay;Mazumdar, Eric et al. (2022) Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
- Zrnic, Tijana;Mazumdar, Eric et al. (2021) Who Leads and Who Follows in Strategic Classification?
- Maheshwari, Chinmay;Chiu, Chih-Yuan et al. (2021) Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
- Yu, Yaodong;Lin, Tianyi et al. (2021) Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization