arXiv Machine Learning
Jul 7
Proportionally Representative Clustering
arXiv:2304. 13917v4 Announce Type: replace Abstract: In recent years, there has been a surge in effort to formalize notions of fairness in machine learning.
FairRARI: A Plug and Play Framework for Fairness-Aware PageRank
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arXiv AI
Jun 2
Demystifying the Optimal Fair Classifier in Multi-Class Classification
arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.
arXiv Machine Learning
Aug 4
Fairness in Augmented Graph Learning: A Survey
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
arXiv Machine Learning
Jul 21
COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering
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.
arXiv Machine Learning
Jul 9
Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models
arXiv:2605. 23145v2 Announce Type: replace-cross Abstract: Individual fairness, the notion that "similar individuals should be treated similarly," provides a strong and flexible fairness guarantee for algorithmic decision makers.
arXiv Machine Learning
Jun 24
FAIRVAR: Fair Federated Learning via Variance Regularization
arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.
arXiv Machine Learning
Jun 26
Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
arXiv:2606. 26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents.
arXiv AI
Aug 17
Training Fair Tabular Foundation Models
arXiv:2608. 14211v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.
arXiv AI
Jun 17
No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems
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 Machine Learning
Jul 16
A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks
arXiv:2603. 21393v2 Announce Type: replace Abstract: The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness.
arXiv AI
Aug 21
Fairness-Aware Network Embeddings: Methods, Applications, and Challenges
arXiv:2608. 19381v1 Announce Type: cross Abstract: Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization.
arXiv Machine Learning
Jun 9
Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints
arXiv:2601. 23221v2 Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort.