6 citations · 41 across the 44 of their papers we have counts for
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cs.GT2025
Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
Benjamin Schiffer, Mark Sellke
In the incentivized exploration model, a principal aims to explore and learn over time by interacting with a sequence of self-interested agents. It has been recently understood tha…
cs.GT2023★ 1 cited
Incentivizing Exploration with Linear Contexts and Combinatorial Actions
Mark Sellke
We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work has sh…
cs.GT2020
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity
Mark Sellke, Aleksandrs Slivkins
We consider incentivized exploration: a version of multi-armed bandits where the choice of arms is controlled by self-interested agents, and the algorithm can only issue recommenda…