109 citations · 266 across the 23 of their papers we have counts for
4 papers · 1 filter
Differentially Private Clustering in Data Streams
Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong
Clustering problems (such as -means and -median) are fundamental unsupervised machine learning primitives, and streaming clustering algorithms have been extensively studied i…
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian +2
Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approxi…
Differentially Private Continual Releases of Streaming Frequency Moment Estimations
Alessandro Epasto, Jieming Mao, Andres Munoz Medina +3
The streaming model of computation is a popular approach for working with large-scale data. In this setting, there is a stream of items and the goal is to compute the desired quant…
Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi +4
We introduce general tools for designing efficient private estimation algorithms, in the high-dimensional settings, whose statistical guarantees almost match those of the best know…