arXiv Machine Learning
Jun 19
Optimal Deterministic Multicalibration and Omniprediction
arXiv:2606. 20557v1 Announce Type: new Abstract: A model is multicalibrated on a collection of group weights $G$ if it is calibrated -- i.
Multicalibration Yields Better Matchings
arXiv:2511. 11413v2 Announce Type: replace Abstract: Consider the problem of finding the best matching in a weighted graph where we only have access to predictions of the actual stochastic weights, based on an underlying context.
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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 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
Jun 4
Optimal Transport under Group Fairness Constraints
arXiv:2601. 07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions.
arXiv Machine Learning
Jun 16
Assessing Predictive Models for Fairness Based on Movement Patterns
arXiv:2605. 23234v3 Announce Type: replace Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations.
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
Jun 2
Beyond Procedure: Substantive Fairness in Conformal Prediction
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
arXiv Machine Learning
Jun 16
Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
arXiv AI
Jun 29
Unbiased Binning for Fairness-aware Attribute Representation
arXiv:2509. 21785v2 Announce Type: replace-cross Abstract: Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset.
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.
arXiv Machine Learning
Jun 9
Generalizing Fair Top-$k$ Selection: An Integrative Approach
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.
arXiv Machine Learning
Jul 7
Probably Correct Optimal Stable Matching under Two-Sided Uncertainty
arXiv:2607. 04824v1 Announce Type: new Abstract: We study a sequential learning problem for stable matchings in two-sided markets where preferences on both sides are initially unknown.