13 citations · 50 across the 15 of their papers we have counts for
6 papers · 1 filter
Understanding Private Evolution as Learning-Augmented Clustering
Audra McMillan, Kunal Talwar, Felix Zhou
Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm, it performs much better in p…
Instance-Optimal Private Density Estimation in the Wasserstein Distance
Vitaly Feldman, Audra McMillan, Satchit Sivakumar +1
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric fo…
Differentially Private Heavy Hitter Detection using Federated Analytics
Karan Chadha, Junye Chen, John Duchi +5
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection. Our model assumes each use…
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Vitaly Feldman, Audra McMillan, Kunal Talwar
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling amplifies differential privacy guarantees of locally ra…
Differentially Private Simple Linear Regression
Daniel Alabi, Audra McMillan, Jayshree Sarathy +2
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortun…
Online Learning via the Differential Privacy Lens
Jacob Abernethy, Young Hun Jung, Chansoo Lee +2
In this paper, we use differential privacy as a lens to examine online learning in both full and partial information settings. The differential privacy framework is, at heart, less…