arXiv:2509. 21785v2 Announce Type: replace-cross Abstract: Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset.
By Abolfazl Asudeh, Zeinab Asoodeh, Bita Asoodeh, Omid Asudeh
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller
arXiv:2607. 18119v1 Announce Type: cross Abstract: Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgroups.
By Kyungseon Lee, Hankyo Jeong, Kunwoong Kim, Kwanho Lee, Yongdai Kim
arXiv:2606. 17810v1 Announce Type: cross Abstract: In this paper, we establish a set of theoretical impossibility results, termed the No-Free-Fairness theorems, that identify three fundamental sources of disparity in learning systems.
By Khoat Than
arXiv:2602.08589v2 Announce Type: replace
Abstract: PageRank (PR) is a fundamental algorithm in graph machine learning tasks. Owing to the increasing importance of algorithmic fairness, we consider t...
By Emmanouil Kariotakis, Aritra Konar
The paper introduces ROME, a framework that learns latent group structure while optimizing worst-group predictive performance. ROME links latent-variable modeling with distributionally robust optimization through an Expectation-Maximization approach for linear models and a neural Mixture-of-Experts for nonlinear settings. Experiments on simulations and three real-world regression datasets show that ROME improves worst-group performance while maintaining competitive overall accuracy compared to existing group-aware and group-label-free robust learning methods.
By Siqi Li, Molei Liu, Yiwei Lyu, Ziye Tian, Chuan Hong, Nan Liu