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A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit
In this paper, we propose an optimistic algorithm and show that the regret is bounded by $O(\sqrt{dT} + \kappa)$, significantly improving the performance over existing methods.
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
This paper studies regret minimization with randomized value functions in reinforcement learning.
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
We study the effect of persistence of engagement on learning in a stochastic multi-armed bandit setting.
Incentivising Exploration and Recommendations for Contextual Bandits with Payments
We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users.
Bandits with Temporal Stochastic Constraints
We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it.
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