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 …

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 …

Many human-facing algorithms--including those that power recommender systems or hiring
decision tools--are trained on data provided by their users. The developers of these …

Motivated by recent developments on meta-learning with linear contextual bandit tasks, we
study the benefit of feature learning in both the multi-task and meta-learning settings. We …

In a carbon auction, licenses for CO2 emissions are allocated among multiple interested
players. Inspired by this setting, we consider repeated multi-unit auctions with uniform …

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 …

Auction-based federated learning (AFL) is an important emerging category of FL incentive
mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners …

We study posted price auctions and dynamic prior-independent mechanisms for (ROI-
constrained) value maximizers. In contrast to classic (quasi-linear) utility maximizers, these …