AI RESEARCH
On Pareto Optimality for Parametric Choice Bandits
arXiv CS.LG
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ArXi:2501.19277v4 Announce Type: replace-cross We study online assortment optimization under stochastic choice when a decision maker simultaneously values cumulative revenue performance and the quality of post-hoc inference on revenue contrasts. We analyze a forced-exploration optimism-in-the-face-of-uncertainty (OFU) scheme that combines two regularized maximum-likelihood estimators: one based on all observations for sequential decision making, and one based only on exploration rounds for inference.