arXiv:2601. 07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2609.36174v1 Announce Type: new Abstract: We consider fair resource allocation in sequential decision-making environments modeled as major-minor weakly coupled Markov decision processes (M2WCMD...
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.
arXiv:2509. 21785v2 Announce Type: replace-cross Abstract: Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset.
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...
arXiv:2606. 18679v1 Announce Type: cross Abstract: We study the problem of fair online resource allocation, motivated by applications such as refugee resettlement and airline scheduling, where agents arrive sequentially and must be assigned to facilities with limited capacities.
The paper introduces “SNSW-Alg”, an algorithm that finds a stable matching maximizing Nash social welfare in the stable marriage problem. It runs in ×O(n^4) time and balances equity while maintaining stability. Experiments across various preference distributions show significant fairness gains with minimal impact on regret, egalitarian criterion, and sex equality, and the resulting matchings are statistically Pareto-undominated by other fairness-based stable matchings.