Bayesian Learning to Select Fair Classifiers

Paper recorded by Signals 4 on 2026-10-14 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.LG · 机器学习 · first seen 2026-10-15

Abstract

Evaluations of classifiers (AI or human or agents) often assume a batch setting in which every agent produces labels on every example in a test set, which can be expensive or impractical in many contexts. This motivates online policies that adaptively select agents to query as examples arrive and update policies as outcomes are observed. We study the problem of selecting, in an online stream, the

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