24 citations · 29 across the 7 of their papers we have counts for
10 papers
Diversity-aware clustering: Computational Complexity and Approximation Algorithms
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti +1
In this work, we study diversity-aware clustering problems where the data points are associated with multiple attributes resulting in intersecting groups. A clustering solution nee…
Fair Column Subset Selection
Antonis Matakos, Bruno Ordozgoiti, Suhas Thejaswi
The problem of column subset selection asks for a subset of columns from an input matrix such that the matrix can be reconstructed as accurately as possible within the span of the…
Generalized Leverage Scores: Geometric Interpretation and Applications
Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis
In problems involving matrix computations, the concept of leverage has found a large number of applications. In particular, leverage scores, which relate the columns of a matrix to…
Clustering with fair-center representation: parameterized approximation algorithms and heuristics
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti +1
We study a variant of classical clustering formulations in the context of algorithmic fairness, known as diversity-aware clustering. In this variant we are given a collection of fa…
Diversity-aware -median : Clustering with fair center representation
Suhas Thejaswi, Bruno Ordozgoiti, Aristides Gionis
We introduce a novel problem for diversity-aware clustering. We assume that the potential cluster centers belong to a set of groups defined by protected attributes, such as ethnici…
Off-the-grid: Fast and Effective Hyperparameter Search for Kernel Clustering
Bruno Ordozgoiti, Lluís A. Belanche Muñoz
Kernel functions are a powerful tool to enhance the -means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dr…
Finding large balanced subgraphs in signed networks
Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis
Signed networks are graphs whose edges are labelled with either a positive or a negative sign, and can be used to capture nuances in interactions that are missed by their unsigned…
Searching for polarization in signed graphs: a local spectral approach
Han Xiao, Bruno Ordozgoiti, Aristides Gionis
Signed graphs have been used to model interactions in social net-works, which can be either positive (friendly) or negative (antagonistic). The model has been used to study polariz…
Discovering Polarized Communities in Signed Networks
Francesco Bonchi, Edoardo Galimberti, Aristides Gionis +2
Signed networks contain edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). The model is simple but powerful and it c…
Reconciliation k-median: Clustering with Non-Polarized Representatives
Bruno Ordozgoiti, Aristides Gionis
We propose a new variant of the k-median problem, where the objective function models not only the cost of assigning data points to cluster representatives, but also a penalty term…