6 citations · 26 across the 22 of their papers we have counts for
4 papers · 1 filter
Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams
Ainesh Bakshi, Vincent Cohen-Addad, Samuel B. Hopkins +2
Metric embeddings are a widely used method in algorithm design, where generally a ``complex'' metric is embedded into a simpler, lower-dimensional one. Historically, the theoretica…
Multi-Swap -Means++
Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi +1
The -means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular -means clustering objective and is know…
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…
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…