1 citations · 4 across the 11 of their papers we have counts for
11 papers
An Efficient Private Algorithm for Community Detection
Vincent Cohen-Addad, Alessandro Epasto, Haim Kaplan +2
In this paper, we study the community detection problem in the stochastic block model (SBM) under privacy constraints. We introduce private and highly efficient algorithms for exac…
Retriever Portfolios: A Principled Approach to Adaptive RAG
Miltiadis Stouras, Vincent Cohen-Addad, Silvio Lattanzi +1
Retrieval-augmented generation (RAG) systems typically rely on a single retriever and a single set of hyperparameters, despite facing highly heterogeneous queries that range from s…
Algorithmic Thinking Theory
MohammadHossein Bateni, Vincent Cohen-Addad, Yuzhou Gu +3
Large language models (LLMs) have proven to be highly effective for solving complex reasoning tasks. Surprisingly, their capabilities can often be improved by iterating on previous…
Almost Optimal Fully Dynamic -Center Clustering with Recourse
Sayan Bhattacharya, Martín Costa, Ermiya Farokhnejad +2
In this paper, we consider the \emph{metric -center} problem in the fully dynamic setting, where we are given a metric space evolving via a sequence of point insertions…
Fully Dynamic -Clustering with Fast Update Time and Small Recourse
Sayan Bhattacharya, Martín Costa, Naveen Garg +2
In the dynamic metric -median problem, we wish to maintain a set of centers in an input metric space that gets updated via point insertions/deletion…
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…
Multi-View Stochastic Block Models
Vincent Cohen-Addad, Tommaso d'Orsi, Silvio Lattanzi +1
Graph clustering is a central topic in unsupervised learning with a multitude of practical applications. In recent years, multi-view graph clustering has gained a lot of attention…
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…
Fully Dynamic -Clustering in Update Time
Sayan Bhattacharya, Martín Costa, Silvio Lattanzi +1
We present a -approximate fully dynamic algorithm for the -median and -means problems on metric spaces with amortized update time and worst-case query tim…
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…