no code implementations • 21 Dec 2023 • Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong
Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning.
no code implementations • 31 May 2023 • Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning.
1 code implementation • 24 May 2023 • Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski
Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset.
no code implementations • 16 Aug 2022 • Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint.
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 • 31 Jan 2022 • Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
Maximizing a monotone submodular function is a fundamental task in machine learning.
no code implementations • NeurIPS 2021 • Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson
In this paper we introduce a new parallel algorithm for the Euclidean hierarchical $k$-median problem that, when using machines with memory $s$ (for $s\in \Omega(\log^2 (n+\Delta+d))$), outputs a hierarchical clustering such that for every fixed value of $k$ the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta)$ factor larger in expectation than that of an optimal solution.
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 • NeurIPS 2021 • Yuchen Wu, Mohammadhossein Bateni, Andre Linhares, Filipe Miguel Goncalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard, Jakab Tardos
The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities".
no code implementations • NeurIPS 2020 • Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson
$k$-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance.
1 code implementation • NeurIPS 2020 • Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
no code implementations • 6 Aug 2018 • Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrović, Amir Zandieh, Aida Mousavifar, Ola Svensson
It is the first low-memory, single-pass algorithm that improves the factor $0. 5$, under the natural assumption that elements arrive in a random order.
no code implementations • ICML 2018 • Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson
It is the first low-memory, singlepass algorithm that improves the factor 0. 5, under the natural assumption that elements arrive in a random order.
no code implementations • NeurIPS 2017 • Slobodan Mitrović, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub Tarnawski, Volkan Cevher
We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm's memory are removed after the stream is finished.