55 citations · 156 across the 20 of their papers we have counts for
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Finite-Time Logarithmic Bayes Regret Upper Bounds
Alexia Atsidakou, Branislav Kveton, Sumeet Katariya +2
We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In a multi-armed bandit, we obtain and upper bounds for…
Selective Uncertainty Propagation in Offline RL
Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya +3
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algo…
Multi-Task Off-Policy Learning from Bandit Feedback
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users…
Bayesian Fixed-Budget Best-Arm Identification
Alexia Atsidakou, Sumeet Katariya, Sujay Sanghavi +1
Fixed-budget best-arm identification (BAI) is a bandit problem where the agent maximizes the probability of identifying the optimal arm within a fixed budget of observations. In th…
Mixed-Effect Thompson Sampling
Imad Aouali, Branislav Kveton, Sumeet Katariya
A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In th…
Deep Hierarchy in Bandits
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Mean rewards of actions are often correlated. The form of these correlations may be complex and unknown a priori, such as the preferences of a user for recommended products and the…