Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

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 …

We study the problem of an online advertising system that wants to optimally spend an
advertiser's given budget for a campaign across multiple platforms, without knowing the …

We study online auto-bidding algorithms for a single advertiser maximizing value under the
Return-on-Spend (RoS) constraint, quantifying performance in terms of regret relative to the …

In Economics, the concept of externality refers to any indirect effect resulting from an
interaction between players and affecting a third party without compensation. Most of the …

We study online learning in repeated first-price auctions where a bidder, only observing the
winning bid at the end of each auction, learns to adaptively bid in order to maximize her …

A crucial problem in modern data science is data-driven algorithm design, where the goal is
to choose the best algorithm, or algorithm parameters, for a specific application domain. In …

We study the problem of regret minimization for a single bidder in a sequence of first-price
auctions where the bidder discovers the item's value only if the auction is won. Our main …

Online auctions are one of the most fundamental facets of the modern economy and power
an industry generating hundreds of billions of dollars a year in revenue. Auction theory has …

The growing adoption of data analytics platforms and machine learning-based solutions for
decision-makers creates a significant demand for datasets, which explains the appearance …