no code implementations • 28 Sep 2023 • Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis
The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation.
no code implementations • 28 Mar 2022 • Yuanyuan Dong, Andrew V. Goldberg, Alexander Noe, Nikos Parotsidis, Mauricio G. C. Resende, Quico Spaen
To solve instances of this size, we develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search (GRASP) framework.
no code implementations • 2 Mar 2022 • Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more.
no code implementations • 15 Jun 2021 • Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining.
no code implementations • 7 Jan 2021 • Giuseppe F. Italiano, Adam Karczmarz, Nikos Parotsidis
In this paper we present an efficient reachability oracle under single-edge or single-vertex failures for planar directed graphs.
Data Structures and Algorithms
1 code implementation • NeurIPS 2019 • Vincent Cohen-Addad, Niklas Oskar D. Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn
This improves over the naive algorithm which consists in recomputing a solution at each time step and that can take up to $O(n^2)$ update time, and $O(n^2)$ total recourse.
no code implementations • NeurIPS 2018 • Fabio Vitale, Nikos Parotsidis, Claudio Gentile
A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e. g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists.
no code implementations • NeurIPS 2017 • Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti
Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced.