286 citations · 534 across the 36 of their papers we have counts for
4 papers · 1 filter
Towards Practical Explainability with Cluster Descriptors
Xiaoyuan Liu, Ilya Tyagin, Hayato Ushijima-Mwesigwa +2
With the rapid development of machine learning, improving its explainability has become a crucial research goal. We study the problem of making the clusters more explainable by inv…
CONFAIR: Configurable and Interpretable Algorithmic Fairness
Ankit Kulshrestha, Ilya Safro
The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world. While the positive impac…
Coping with Mistreatment in Fair Algorithms
Ankit Kulshrestha, Ilya Safro
Machine learning actively impacts our everyday life in almost all endeavors and domains such as healthcare, finance, and energy. As our dependence on the machine learning increases…
AML-SVM: Adaptive Multilevel Learning with Support Vector Machines
Ehsan Sadrfaridpour, Korey Palmer, Ilya Safro
The support vector machines (SVM) is one of the most widely used and practical optimization based classification models in machine learning because of its interpretability and flex…