AI RESEARCH
REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
arXiv CS.LG
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ArXi:2603.22750v1 Announce Type: cross Active learning reduces labeling costs by selecting samples that maximize information gain. A dominant framework, Query-by-Committee (QBC), typically relies on perturbation-based diversity by inducing model disagreement through random feature subsetting or data blinding. While this approximates one notion of epistemic uncertainty, it sacrifices direct characterization of the plausible hypothesis space.