activity
20202026
most citedReaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions

2 citations · 8 across the 17 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

cs.DS2024

Sparsest cut and eigenvalue multiplicities on low degree Abelian Cayley graphs

Tommaso d'Orsi, Chris Jones, Jake Ruotolo +2

Whether or not the Sparsest Cut problem admits an efficient -approximation algorithm is a fundamental algorithmic question with connections to geometry and the Unique Games C…

cs.LG2024★ 1 cited

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…

cs.DS2024

A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering

Vincent Cohen-Addad, Tommaso d'Orsi, Aida Mousavifar

We consider the semi-random graph model of [Makarychev, Makarychev and Vijayaraghavan, STOC'12], where, given a random bipartite graph with edges and an unknown bipartition $(A…

cs.LG2024

Perturb-and-Project: Differentially Private Similarities and Marginals

Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto +2

We revisit the input perturbations framework for differential privacy where noise is added to the input and the result is then projected back to the space of adm…

cs.DS2024

Private graphon estimation via sum-of-squares

Hongjie Chen, Jingqiu Ding, Tommaso d'Orsi +3

We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant numbe…

cs.DS2024

Max-Cut with -Accurate Predictions

Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta +2

We study the approximability of the MaxCut problem in the presence of predictions. Specifically, we consider two models: in the noisy predictions model, for each vertex we are give…