39 citations · 63 across the 19 of their papers we have counts for
18 papers
Making Old Things New: A Unified Algorithm for Differentially Private Clustering
Max Dupré la Tour, Monika Henzinger, David Saulpic
As a staple of data analysis and unsupervised learning, the problem of private clustering has been widely studied under various privacy models. Centralized differential privacy is…
Tighter Bounds for Local Differentially Private Core Decomposition and Densest Subgraph
Monika Henzinger, A. R. Sricharan, Leqi Zhu
Computing the core decomposition of a graph is a fundamental problem that has recently been studied in the differentially private setting, motivated by practical applications in da…
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…
Deterministic Near-Linear Time Minimum Cut in Weighted Graphs
Monika Henzinger, Jason Li, Satish Rao +1
In 1996, Karger [Kar96] gave a startling randomized algorithm that finds a minimum-cut in a (weighted) graph in time which he termed near-linear time meaning linear (…
Experimental Evaluation of Fully Dynamic k-Means via Coresets
Monika Henzinger, David Saulpic, Leonhard Sidl
For a set of points in , the Euclidean -means problems consists of finding centers such that the sum of distances squared from each data point to its closest c…
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+…