6 citations · 26 across the 22 of their papers we have counts for
10 papers · 1 filter
Approximating High-Dimensional Earth Mover's Distance as Fast as Closest Pair
Lorenzo Beretta, Vincent Cohen-Addad, Rajesh Jayaram +1
We give a reduction from -approximate Earth Mover's Distance (EMD) to -approximate Closest Pair (CP). As a consequence, we improve the fastest kno…
Scalable contribution bounding to achieve privacy
Vincent Cohen-Addad, Alessandro Epasto, Jason Lee +1
In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bou…
Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto +1
In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to…
Dynamic Correlation Clustering in Sublinear Update Time
Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori +1
We study the classic problem of correlation clustering in dynamic node streams. In this setting, nodes are either added or randomly deleted over time, and each node pair is connect…
A Scalable Algorithm for Individually Fair K-means Clustering
MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto +1
We present a scalable algorithm for the individually fair (, )-clustering problem introduced by Jung et al. and Mahabadi et al. Given points in a metric space, let $δ…
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…
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…
Scalable Differentially Private Clustering via Hierarchically Separated Trees
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi +5
We study the private -median and -means clustering problem in dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algor…
Improved Approximations for Euclidean -means and -median, via Nested Quasi-Independent Sets
Vincent Cohen-Addad, Hossein Esfandiari, Vahab Mirrokni +1
Motivated by data analysis and machine learning applications, we consider the popular high-dimensional Euclidean -median and -means problems. We propose a new primal-dual alg…
Correlation Clustering in Constant Many Parallel Rounds
Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović +3
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining. In correlation clustering, one receives as input a signed graph an…