6 citations · 12 across the 9 of their papers we have counts for
4 papers · 1 filter
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits
Woojin Jeong, Seungki Min
We consider a Bayesian budgeted multi-armed bandit problem, in which each arm consumes a different amount of resources when selected and there is a budget constraint on the total a…
An Information-Theoretic Analysis of Nonstationary Bandit Learning
Seungki Min, Daniel Russo
In nonstationary bandit learning problems, the decision-maker must continually gather information and adapt their action selection as the latent state of the environment evolves. I…
Policy Gradient Optimization of Thompson Sampling Policies
Seungki Min, Ciamac C. Moallemi, Daniel J. Russo
We study the use of policy gradient algorithms to optimize over a class of generalized Thompson sampling policies. Our central insight is to view the posterior parameter sampled by…
Thompson Sampling with Information Relaxation Penalties
Seungki Min, Costis Maglaras, Ciamac C. Moallemi
We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we defi…