279 citations · 1.4k across the 46 of their papers we have counts for
7 papers · 1 filter
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers
Lunjia Hu, Omer Reingold
We study the problem of robustly estimating the mean of a -dimensional distribution given examples, where most coordinates of every example may be missing and $\varepsilon N…
Online Assignment of Heterogeneous Tasks in Crowdsourcing Markets
Sepehr Assadi, Justin Hsu, Shahin Jabbari
We investigate the problem of heterogeneous task assignment in crowdsourcing markets from the point of view of the requester, who has a collection of tasks. Workers arrive online o…
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…
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…
Differential Privacy for the Analyst via Private Equilibrium Computation
Justin Hsu, Aaron Roth, Jonathan Ullman
We give new mechanisms for answering exponentially many queries from multiple analysts on a private database, while protecting differential privacy both for the individuals in the…
Distributed Private Heavy Hitters
Justin Hsu, Sanjeev Khanna, Aaron Roth
In this paper, we give efficient algorithms and lower bounds for solving the heavy hitters problem while preserving differential privacy in the fully distributed local model. In th…