2 citations · 6 across the 18 of their papers we have counts for
3 papers · 1 filter
Correlated Noise Mechanisms for Differentially Private Learning
Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo +9
This monograph explores the design and analysis of correlated noise mechanisms for differential privacy (DP), focusing on their application to private training of AI and machine le…
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger +5
We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new d…
Simple, Scalable and Effective Clustering via One-Dimensional Projections
Moses Charikar, Monika Henzinger, Lunjia Hu +2
Clustering is a fundamental problem in unsupervised machine learning with many applications in data analysis. Popular clustering algorithms such as Lloyd's algorithm and -means+…