REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees

摘要

Active learning reduces labeling costs by querying samples to maximize information gain. A dominant framework, Query-by-Committee, 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. We propose the complementary approach: *Rashomon Ensembled Active Learning (REAL)* which constructs a committee by exhaustively enumerating the Rashomon Set of all near-optimal models. To address functional redundancy within this set, we adopt a Probably Approximately Correct-Bayesian framework using a Gibbs posterior to weigh committee members by their empirical risk. Leveraging recent algorithmic advances, we exactly enumerate this set for the class of sparse decision trees. Across synthetic and established active learning baselines, REAL outperforms randomized ensembles, particularly in moderately noisy environments where it leverages expanded model multiplicity to achieve faster convergence.