7 papers
Learning Distributions from Multiple Data Providers
Jon Kleinberg, Amin Saberi, Xizhi Tan +1
Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples. The goal is to lear…
Approximating Gains-from-Trade in Matching Markets
Moshe Babaioff, Aviad Rubinstein, Xizhi Tan +1
A central challenge in mechanism design is to develop truthful trade mechanisms that maximize the expected gains-from-trade (GFT) in two-sided markets with strategic agents. As ach…
Procurement Auctions with Predictions: Improved Frugality for Facility Location
Eric Balkanski, Nicholas DeFilippis, Vasilis Gkatzelis +1
We study the problem of designing procurement auctions for the strategic uncapacitated facility location problem: a company needs to procure a set of facility locations in order to…
Clock Auctions Augmented with Unreliable Advice
Vasilis Gkatzelis, Daniel Schoepflin, Xizhi Tan
We provide the first analysis of (deferred acceptance) clock auctions in the learning-augmented framework. These auctions satisfy a unique list of appealing properties, including o…
Learning-Augmented Metric Distortion via -Veto Core
Ben Berger, Michal Feldman, Vasilis Gkatzelis +1
In the metric distortion problem there is a set of candidates and voters in the same metric space. The goal is to select a candidate minimizing the social cost: the sum of…
Getting More by Knowing Less: Bayesian Incentive Compatible Mechanisms for Fair Division
Vasilis Gkatzelis, Alexandros Psomas, Xizhi Tan +1
We study fair resource allocation with strategic agents. It is well-known that, across multiple fundamental problems in this domain, truthfulness and fairness are incompatible. For…