Random cuts are optimal for explainable k-medians
K Makarychev, L Shan - Advances in Neural Information …, 2024 - proceedings.neurips.cc
We show that the RandomCoordinateCut algorithm gives the optimal competitive ratio for
explainable $ k $-medians in $\ell_1 $. The problem of explainable $ k $-medians was …
explainable $ k $-medians in $\ell_1 $. The problem of explainable $ k $-medians was …
A Framework for Data-Driven Explainability in Mathematical Optimization
Advancements in mathematical programming have made it possible to efficiently tackle
large-scale real-world problems that were deemed intractable just a few decades ago …
large-scale real-world problems that were deemed intractable just a few decades ago …
Explaining Kernel Clustering via Decision Trees
M Fleissner, LC Vankadara… - arXiv preprint arXiv …, 2024 - arxiv.org
Despite the growing popularity of explainable and interpretable machine learning, there is
still surprisingly limited work on inherently interpretable clustering methods. Recently, there …
still surprisingly limited work on inherently interpretable clustering methods. Recently, there …
Fairness and Explainability in Clustering Problems
X Jia - 2023 - infoscience.epfl.ch
In this thesis we present and analyze approximation algorithms for three different clustering
problems. The formulations of these problems are motivated by fairness and explainability …
problems. The formulations of these problems are motivated by fairness and explainability …
Approximation Algorithms for Explainable Clustering
L Shan - 2023 - search.proquest.com
Clustering is a fundamental task in unsupervised learning, which aims to partition the data
set into several clusters. It is widely used for data mining, image segmentation, and natural …
set into several clusters. It is widely used for data mining, image segmentation, and natural …