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 …

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

We study the aggregate welfare and individual regret guarantees of dynamic\emph {pacing
algorithms} in the context of repeated auctions with budgets. Such algorithms are commonly …

We study the liquid welfare in sequential first-price auctions with budget-limited buyers. We
focus on first-price auctions, which are increasingly commonly used in many settings, and …

Budget constraints are ubiquitous in online advertisement auctions. To manage these
constraints and smooth out the expenditure across auctions, the bidders (or the platform on …

Learning to bid in repeated first-price auctions is a fundamental problem at the interface of
game theory and machine learning, which has seen a recent surge in interest due to the …

In this survey, we summarize recent developments in research fueled by the growing
adoption of automated bidding strategies in online advertising. We explore the challenges …

We study the efficiency of non-truthful auctions for auto-bidders with both return on spend
(ROS) and budget constraints. The efficiency of a mechanism is measured by the price of …

In online advertising markets, budget-constrained advertisers acquire ad placements
through repeated bidding in auctions on various platforms. We present a strategy for bidding …

In this work, we investigate the problem of how to bid in repeated contextual first price
auctions for a single learner (the bidder). Concretely, at each time t, the learner receives a …