1 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit
Priyank Agrawal, Theja Tulabandhula, Vashist Avadhanula
In this paper, we consider the contextual variant of the MNL-Bandit problem. More specifically, we consider a dynamic set optimization problem, where a decision-maker offers a subs…
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
Priyank Agrawal, Jinglin Chen, Nan Jiang
This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping varia…
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
Priyank Agrawal, Theja Tulabandhula
We study the effect of persistence of engagement on learning in a stochastic multi-armed bandit setting. In advertising and recommendation systems, repetition effect includes a wea…
Incentivising Exploration and Recommendations for Contextual Bandits with Payments
Priyank Agrawal, Theja Tulabandhula
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. By using payments to incentivize thes…