Certifying Model Upgrades with Slice-Wise Non-Regression and Incumbent Fallback
An updated model can improve an aggregate metric while degrading a slice that matters to a downstream user. We study checkpoint selection subject to non-regression tolerances relative to a retained incumbent. The central distinction is between failing to detect harm and certifying non-inferiority: the former can release harmful updates with high probability when evaluation is noisy. We give a reproducible release procedure that separates candidate search from independent, paired evaluation and returns the exact incumbent when certification fails. Applying established intersection-union and Learn-then-Test principles, we state finite-sample guarantees for one frozen candidate, a finite can...
| Authors | Shengwei Zhang, Tao Wu, Fei Qian |
|---|---|
| Primary category | cs.LG |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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