239 citations · 355 across the 14 of their papers we have counts for
4 papers · 1 filter
How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time
Omar Besbes, Omar Mouchtaki
We study the classical newsvendor problem in which the decision-maker must trade-off underage and overage costs. In contrast to the typical setting, we assume that the decision-mak…
Contextual Inverse Optimization: Offline and Online Learning
Omar Besbes, Yuri Fonseca, Ilan Lobel
We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action a…
Optimal Pricing with a Single Point
Amine Allouah, Achraf Bahamou, Omar Besbes
We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on data at a single historical price? How valuable is such d…
Mechanism Design under Approximate Incentive Compatibility
Santiago Balseiro, Omar Besbes, Francisco Castro
A fundamental assumption in classical mechanism design is that buyers are perfect optimizers. However, in practice, buyers may be limited by their computational capabilities or a l…