A monopolist platform uses data to match heterogeneous consumers with multiproduct
sellers. The consumers can purchase the products on the platform or search off the platform …

In classic auction theory, reserve prices are known to be effective for improving revenue for
the auctioneer against quasi-linear utility maximizing bidders. The introduction of reserve …

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 …

Auto-bidding is an area of increasing importance in the domain of online advertising. We
study the problem of designing auctions in an auto-bidding setting with the goal of …

Abstract In Spring 2005, the Ombud for Equal Treatment in Austria launched a campaign
notifying employers and newspapers that gender preferences in job ads were illegal. At 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 …

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 …

We study the price of anarchy of the first-price auction in the autobidding world, where
bidders can be either utility maximizers (ie, traditional bidders) or value maximizers (ie …

Auto-bidding plays a crucial role in facilitating online advertising by automatically providing
bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding …