Add to ORKGWe study the question of fair clustering under the disparate impact doctrine, where each protected class must have approximately equal representation in every cluster. We formulate the fair clustering problem under both the k-center and the k-median objectives, and show that even with two protected classes the problem is challenging, as the optimum solution can violate common conventions - for instance a point may no longer be assigned to its nearest cluster center! En route we introduce the concept of fairlets, which are minimal sets that satisfy fair representation while approximately preserving the clustering objective. We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing ma...
Producción CientíficaWe consider the problem of diversity enhancing clustering, i.e, developing clus...
Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering...
We propose a general variational framework of fair clustering, which integrates an original Kullback...
As algorithms play a large role in our decision making, the possibility of algorithmic bias has led ...
The study of algorithmic fairness received growing attention recently. This stems from the awareness...
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that feature ubiq...
We explore the area of fairness in clustering from the different perspective of modifying clustering...
We study fair center based clustering problems. In an influential paper, Chierichetti, Kumar, Lattan...
In the last few years, the need of preventing classification biases due to race, gender, social stat...
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing un...
We introduce a novel problem for diversity-aware clustering. We assume that the potential cluster ce...
Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and may...
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points hav...
Clustering problems and clustering algorithms are often overly sensitive to the presence of outliers...
We consider the family of Correlation Clustering optimization problems under fairness constraints. I...
Producción CientíficaWe consider the problem of diversity enhancing clustering, i.e, developing clus...
Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering...
We propose a general variational framework of fair clustering, which integrates an original Kullback...
As algorithms play a large role in our decision making, the possibility of algorithmic bias has led ...
The study of algorithmic fairness received growing attention recently. This stems from the awareness...
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that feature ubiq...
We explore the area of fairness in clustering from the different perspective of modifying clustering...
We study fair center based clustering problems. In an influential paper, Chierichetti, Kumar, Lattan...
In the last few years, the need of preventing classification biases due to race, gender, social stat...
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing un...
We introduce a novel problem for diversity-aware clustering. We assume that the potential cluster ce...
Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and may...
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points hav...
Clustering problems and clustering algorithms are often overly sensitive to the presence of outliers...
We consider the family of Correlation Clustering optimization problems under fairness constraints. I...
Producción CientíficaWe consider the problem of diversity enhancing clustering, i.e, developing clus...
Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering...
We propose a general variational framework of fair clustering, which integrates an original Kullback...