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Georgios Birmpas

6 accepted papers

2022

Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond

NeurIPS 2022accept

In most social choice settings, the participating agents express their preferences over the different alternatives in the form of linear orderings. While this clearly simplifies preference elicitation, it inevitably leads to poor performance with respect to optimizing a cardinal objective, such as t…

Cited by 14SourcePDFScholar
2022

Fair Division of Indivisible Goods: A Survey

IJCAI 2022poster

Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions regardin…

Cited by 105SourcePDFScholar
2022

Fair Equilibria in Sponsored Search Auctions: The Advertisers’ Perspective

IJCAI 2022poster

In this work we introduce a new class of mechanisms composed of a traditional Generalized Second Price (GSP) auction, and a fair division scheme in order to achieve some desired level of fairness between groups of Bayesian strategic advertisers. We propose two mechanisms, beta-Fair GSP and GSP-EFX,…

Cited by 3SourcePDFScholar
2021

A Few Queries Go a Long Way: Information-Distortion Tradeoffs in Matching

AAAI 2021technical

We consider the one-sided matching problem, where n agents have preferences over n items, and these preferences are induced by underlying cardinal valuation functions. The goal is to match every agent to a single item so as to maximize the social welfare. Most of the related literature, however, ass…

Cited by 33SourcePDFScholar
2020

Maximum Nash Welfare and Other Stories About EFX

IJCAI 2020poster

We consider the classic problem of fairly allocating indivisible goods among agents with additive valuation functions and explore the connection between two prominent fairness notions: maximum Nash welfare (MNW) and envy-freeness up to any good (EFX). We establish that an MNW allocation is always EF…

Cited by 0SourcePDFScholar
2020

Optimally Deceiving a Learning Leader in Stackelberg Games

NeurIPS 2020poster

Recent results in the ML community have revealed that learning algorithms used to compute the optimal strategy for the leader to commit to in a Stackelberg game, are susceptible to manipulation by the follower. Such a learning algorithm operates by querying the best responses or the payoffs of the f…

Cited by 21SourcePDFScholar