arXiv Machine Learning
Multigroup Fairness and Omniprediction: Separations and Equivalences
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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.
arXiv Machine Learning
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Generalized and Unified Equivalences between Hardness and Pseudoentropy
arXiv:2507. 05972v3 Announce Type: replace-cross Abstract: Pseudoentropy characterizations give quantitatively precise formulations of the relationship between computational hardness and computational randomness.
arXiv Machine Learning
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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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Unfair Utilities and First Steps Towards Improving Them
arXiv:2306.00636v3 Announce Type: replace-cross Abstract: Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the...
arXiv Machine Learning
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When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness
The paper argues that traditional probabilistic fairness metrics can miss significant disparities in the actual consequences of decisions. By introducing a utility-based framework, the authors show that a process can satisfy ε-fairness yet still be maximally unfair when utilities are considered. They apply this framework to college admissions and credit‑risk assessment, demonstrating that equalizing probabilities alone may mask unequal utility outcomes across groups.
arXiv Machine Learning
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Generalizing Fair Top-$k$ Selection: An Integrative Approach
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