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 …

In online advertising markets, advertisers often purchase ad placements through bidding in
repeated auctions based on realized viewer information. We study how budget-constrained …

We consider the problem of optimal bidding for virtual trading in two-settlement electricity
markets. A virtual trader aims to arbitrage on differences between day-ahead and real-time …

Contextual dynamic pricing aims to set personalized prices based on sequential interactions
with customers. At each time period, a customer who is interested in purchasing a product …

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 online advertising markets, setting budget and return on investment (ROI) constraints are
two prevalent ways to help advertisers (ie buyers) utilize limited monetary resources …

Bilateral trade models the problem of intermediating between two rational agents—a seller
and a buyer—both characterized by a private valuation for an item they want to trade. We …

First-price auctions have very recently swept the online advertising industry, replacing
second-price auctions as the predominant auction mechanism on many platforms. This shift …

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 …