7 citations · 29 across the 42 of their papers we have counts for
4 papers · 1 filter
Adversarial Dueling Bandits
Aadirupa Saha, Tomer Koren, Yishay Mansour
We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only…
Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards
Aadirupa Saha, Pierre Gaillard, Michal Valko
In this paper, we consider the problem of sleeping bandits with stochastic action sets and adversarial rewards. In this setting, in contrast to most work in bandits, the actions ma…
Best-item Learning in Random Utility Models with Subset Choices
Aadirupa Saha, Aditya Gopalan
We consider the problem of PAC learning the most valuable item from a pool of items using sequential, adaptively chosen plays of subsets of items, when, upon playing a subs…
Regret Minimization in Stochastic Contextual Dueling Bandits
Aadirupa Saha, Aditya Gopalan
We consider the problem of stochastic -armed dueling bandit in the contextual setting, where at each round the learner is presented with a context set of items, each represe…