2 papers
cs.LG2025
FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance
Minh Phu Vuong, Young-Ju Lee, Iván Ojeda-Ruiz +1
Due to the growing concern about unsavory behaviors of machine learning models toward certain demographic groups, the notion of 'fairness' has recently drawn much attention from th…
cs.LG2025
Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints
Iván Ojeda-Ruiz, Young Ju Lee, Malcolm Dickens +1
Recent research has focused on mitigating algorithmic bias in clustering by incorporating fairness constraints into algorithmic design. Notions such as disparate impact, community…