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arXiv Machine Learning By Jackson Eshbaugh, Jorge Silveyra

Align Before You Combine: Reference Space Calibration for Supervision Without Ground Truth

Read the original on arXiv Machine Learning →

The paper proposes a calibration-first framework that generates supervision scores without ground-truth labels or a shared annotation space. It aligns subset-specific scorers by mapping them onto a synthetic ordinal reference space built from ordered calibration features, enabling the fusion of otherwise incomparable outputs. Experiments on three benchmark datasets show that this method consistently outperforms uncalibrated averaging and matches or exceeds the best individual scorer, with significant improvements after Bonferroni correction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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