Contextual Pandora’s Box

Pandora’s Box is a fundamental stochastic optimization problem, where the decision-maker must find a good alternative, while minimizing the search cost of exploring the value of each alternative. In the original formulation, it is assumed that accurate distributions are given for the values of all the alternatives, while recent work studies the online variant of Pandora’s Box where the distributions are originally unknown. In this work, we study Pandora’s Box in the online setting, while incorporating context. At each round, we are presented with a number of alternatives each having a context, an exploration cost and an unknown value drawn from an unknown distribution that may change at every round. Our main result is a no-regret algorithm that performs comparably well against the optimal algorithm which knows all prior distributions exactly. Our algorithm works even in the bandit setting where the algorithm never learns the values of the alternatives that were not explored. The key technique that enables our result is a novel modification of the realizability condition in contextual bandits that connects a context to a sufficient statistic of each alternative’s distribution (its reservation value) rather than its mean.

Authors

• Alexia Atsidakou University of Texas - Austin
• Constantine Caramanis University of Texas - Austin
• Evangelia Gergatsouli University of Wisconsin - Madison Georgia Institute of Technology
• Orestis Papadigenopoulos Columbia University
• Christos Tzamos University of Wisconsin - Madison University of Athens

Keywords

• ML: Online Learning & Bandits
• RU: Sequential Decision Making