We study two-sided matching markets in which one side of the market (the players) does not
have a priori knowledge about its preferences for the other side (the arms) and is required to …

Stable matching, a classical model for two-sided markets, has long been studied assuming
known preferences. In reality agents often have to learn about their preferences through …

We explore a new model of bandit experiments where a potentially nonstationary sequence
of contexts influences arms' performance. Context-unaware algorithms risk confounding …

Classical approaches to experimental design assume that intervening on one unit does not
affect other units. There are many important settings, however, where this noninterference …

Online learning in a two-sided matching market, with demand side agents continuously
competing to be matched with supply side (arms), abstracts the complex interactions under …

Service platforms must determine rules for matching heterogeneous demand (customers)
and supply (workers) that arrive randomly over time and may be lost if forced to wait too long …

Problem definition: Platforms critically rely on rating systems to learn the quality of market
participants. In practice, however, ratings are often highly inflated and therefore, not very …

Problem definition: We study personalized product recommendations on platforms when
customers have unknown preferences. Importantly, customers may disengage when offered …

We study a novel variant of the multi-armed bandit problem, where at each time step, the
player observes an independently sampled context that determines the arms' mean rewards …

This paper proposes and evaluates a new hierarchical approach to peer-to-peer logistics
platforms, recasting the platform's role as one providing personalized menus of requests to …