560 citations · 2.1k across the 38 of their papers we have counts for
4 papers · 1 filter
Predicting with Distributions
Michael Kearns, Zhiwei Steven Wu
We consider a new learning model in which a joint distribution over vector pairs is determined by an unknown function that maps input vectors not to individual o…
Privacy for the Protected (Only)
Michael Kearns, Aaron Roth, Zhiwei Steven Wu +1
Motivated by tensions between data privacy for individual citizens, and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a compu…
Privacy and Truthful Equilibrium Selection for Aggregative Games
Rachel Cummings, Michael Kearns, Aaron Roth +1
We study a very general class of games --- multi-dimensional aggregative games --- which in particular generalize both anonymous games and weighted congestion games. For any such g…
Online Learning and Profit Maximization from Revealed Preferences
Kareem Amin, Rachel Cummings, Lili Dworkin +2
We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant accordi…