381 citations · 2.3k across the 89 of their papers we have counts for
5 papers · 2 filters
Jointly Private Convex Programming
Justin Hsu, Zhiyi Huang, Aaron Roth +1
In this paper we present an extremely general method for approximately solving a large family of convex programs where the solution can be divided between different agents, subject…
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…
Privately Solving Linear Programs
Justin Hsu, Aaron Roth, Tim Roughgarden +1
In this paper, we initiate the systematic study of solving linear programs under differential privacy. The first step is simply to define the problem: to this end, we introduce sev…
Dual Query: Practical Private Query Release for High Dimensional Data
Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu +2
We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets. Like all algorithms for this task, ours necessarily h…