1 citations · 1 across the 2 of their papers we have counts for
5 papers
Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location
Priyank Agrawal, Eric Balkanski, Vasilis Gkatzelis +2
In this work we introduce an alternative model for the design and analysis of strategyproof mechanisms that is motivated by the recent surge of work in "learning-augmented algorith…
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…
Bandits with Temporal Stochastic Constraints
Priyank Agrawal, Theja Tulabandhula
We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for a…