5 papers
Lexicographically Fair Learning: Algorithms and Generalization
Emily Diana, Wesley Gill, Ira Globus-Harris +3
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short). Informally, given a…
Minimax Group Fairness: Algorithms and Experiments
Emily Diana, Wesley Gill, Michael Kearns +2
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes.…
Algorithms and Learning for Fair Portfolio Design
Emily Diana, Travis Dick, Hadi Elzayn +5
We consider a variation on the classical finance problem of optimal portfolio design. In our setting, a large population of consumers is drawn from some distribution over risk tole…
Differentially Private Call Auctions and Market Impact
Emily Diana, Hadi Elzayn, Michael Kearns +3
We propose and analyze differentially private (DP) mechanisms for call auctions as an alternative to the complex and ad-hoc privacy efforts that are common in modern electronic mar…
Optimal, Truthful, and Private Securities Lending
Emily Diana, Michael Kearns, Seth Neel +1
We consider a fundamental dynamic allocation problem motivated by the problem of in financial markets, the mechanism underlying the short selling of s…