Sotabase

Career

· Assistant Professor in Computing and Mathematical Sciences and Economics at Caltech, UC Berkeley2021–
· PhD in Electrical Engineering and Computer Science, UC Berkeley2015–

Publications (55)

On Gradient-Based Learning in Continuous Games
SIAM Journal on Mathematics of Data Science · 2018
151
cited
On Finding Local Nash Equilibria (and only Local Nash Equilibria) in Zero-Sum Games
ACM / IMS Journal of Data Science · 2019
104
cited
Who Leads and Who Follows in Strategic Classification?
Neural Information Processing Systems · 2021
64
cited
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games
Adaptive Agents and Multi-Agent Systems · 2019
44
cited
Langevin Monte Carlo for Contextual Bandits
International Conference on Machine Learning · 2022
33
cited
On Approximate Thompson Sampling with Langevin Algorithms
International Conference on Machine Learning · 2020
32
cited
Algorithmic Collective Action in Machine Learning
International Conference on Machine Learning · 2023
31
cited
Inverse Risk-Sensitive Reinforcement Learning
IEEE Transactions on Automatic Control · 2017
31
cited
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
International Conference on Artificial Intelligence and Statistics · 2021
29
cited
Convergence Analysis of Gradient-Based Learning in Continuous Games
Conference on Uncertainty in Artificial Intelligence · 2019
28
cited
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
International Conference on Artificial Intelligence and Statistics · 2021
28
cited
Feedback Linearization for Uncertain Systems via Reinforcement Learning
IEEE International Conference on Robotics and Automation · 2020
28
cited
Feedback Linearization for Unknown Systems via Reinforcement Learning
arXiv.org · 2019
28
cited
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
Neural Information Processing Systems · 2021
26
cited
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
Neural Information Processing Systems · 2022
19
cited
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
International Conference on Machine Learning · 2024
16
cited
To observe or not to observe: Queuing game framework for urban parking
IEEE Conference on Decision and Control · 2016
16
cited
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
Neural Information Processing Systems · 2023
13
cited
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings
arXiv.org · 2019
13
cited
Gradient-based inverse risk-sensitive reinforcement learning
IEEE Conference on Decision and Control · 2017
12
cited
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Eric Mazumdar | Researcher Profile | Sotabase | Sotabase