9.7k citations · 18.6k across the 63 of their papers we have counts for
68 papers
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan +1
A key challenge in many modern data analysis tasks is that user data are heterogeneous. Different users may possess vastly different numbers of data points. More importantly, it ca…
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…
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar, Shan Wang, Audra McMillan +34
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private…
Fast Optimal Locally Private Mean Estimation via Random Projections
Hilal Asi, Vitaly Feldman, Jelani Nelson +2
We study the problem of locally private mean estimation of high-dimensional vectors in the Euclidean ball. Existing algorithms for this problem either incur sub-optimal error or ha…
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Hilal Asi, Vitaly Feldman, Tomer Koren +1
We consider online learning problems in the realizable setting, where there is a zero-loss solution, and propose new Differentially Private (DP) algorithms that obtain near-optimal…
Concentration of the Langevin Algorithm's Stationary Distribution
Jason M. Altschuler, Kunal Talwar
A canonical algorithm for log-concave sampling is the Langevin Algorithm, aka the Langevin Diffusion run with some discretization stepsize . This discretization leads the Lan…