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arXiv Machine Learning By Dwi Sunaryono

BRIDGE: An Acceptance Gate to Block False Improvements in Model Updates, with a Retrospective Clinical Evaluation

Read the original on arXiv Machine Learning →

BRIDGE is a model‑agnostic acceptance gate that filters model updates by cross‑fitting and bootstrap bounds, aiming to prevent false improvements that arise when validation gains do not translate to new data. Across multiple settings—including 11 new datasets, synthetic tasks, 12 retrospective clinical tasks, and 39 supporting datasets—BRIDGE reduces false‑improvement rates from roughly 19–25% with a naive rule to as low as 0–1.1%. The method also yields higher mean gains per deployed update, achieving 1.8 to 3.1 times the naive rule’s performance.

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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