6 citations · 16 across the 4 of their papers we have counts for
4 papers
Learning to Steer Learners in Games
Yizhou Zhang, Yi-An Ma, Eric Mazumdar
We consider the problem of learning to exploit learning algorithms through repeated interactions in games. Specifically, we focus on the case of repeated two player, finite-action…
Langevin Monte Carlo for Contextual Bandits
Pan Xu, Hongkai Zheng, Eric Mazumdar +2
We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distrib…
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
Yaodong Yu, Tianyi Lin, Eric Mazumdar +1
Distributionally robust supervised learning (DRSL) is emerging as a key paradigm for building reliable machine learning systems for real-world applications -- reflecting the need f…
On Thompson Sampling with Langevin Algorithms
Eric Mazumdar, Aldo Pacchiano, Yi-an Ma +2
Thompson sampling for multi-armed bandit problems is known to enjoy favorable performance in both theory and practice. However, it suffers from a significant limitation computation…