We study online learning problems in which a decision maker has to take a sequence of
decisions subject to $ m $ long-term constraints. The goal of the decision maker is to …

In digital online advertising, advertisers procure ad impressions simultaneously on multiple
platforms, or so-called channels, such as Google Ads, Meta Ads Manager, etc., each of …

Budget management strategies in repeated auctions have received growing attention in
online advertising markets. However, previous work on budget management in online …

We study a game between autobidding algorithms that compete in an online advertising
platform. Each autobidder is tasked with maximizing its advertiser's total value over multiple …

We consider contextual bandits with linear constraints (CBwLC), a variant of contextual
bandits in which the algorithm consumes multiple resources subject to linear constraints on …

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 …

We consider the problem of repeatedly auctioning a single item to multiple iid buyers who
each use a no-regret learning algorithm to bid over time. In particular, we study the seller's …

We study online learning problems in which a decision maker has to make a sequence of
costly decisions, with the goal of maximizing their expected reward while adhering to budget …

The increasing availability of real-time data has fueled the prevalence of algorithmic bidding
(or autobidding) in online advertising markets, and has enabled online ad platforms to …

Today's online advertisers procure digital ad impressions through interacting with
autobidding platforms: advertisers convey high level procurement goals via setting levers …