50 citations · 80 across the 6 of their papers we have counts for
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cs.LG2020★ 5 cited
Meta-Learning Bandit Policies by Gradient Ascent
Branislav Kveton, Martin Mladenov, Chih-Wei Hsu +3
Most bandit policies are designed to either minimize regret in any problem instance, making very few assumptions about the underlying environment, or in a Bayesian sense, assuming…
cs.LG2020★ 5 cited
Differentiable Bandit Exploration
Craig Boutilier, Chih-Wei Hsu, Branislav Kveton +3
Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution . In this work, we learn such policies for an…