Conformal Candidate Certification for Offline Model-Based Optimization

Abstract:Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset. Because candidates are deliberately out-of-distribution, surrogate rankings are least reliable exactly where the optimizer is most aggressive, yet existing methods provide no per-candidate statistical certificate that a design meets a target threshold. We propose \emph{Conformal Candidate Certification} (CCC), a post-hoc wrapper that attaches a calibrated one-sided lower bound to each candidate and advances only those whose bound exceeds the target. We show that entropy-regularized surrogate maximization induces a Gibbs-tilted proposal, so the same surrogate supplies importance weights for weighted conformal prediction without a separate density-ratio estimation step. In a controlled synthetic study, CCC certifies $16.7\%$ of an aggressive proposal pool with empirical coverage 0.990 at nominal 0.90, while standard conformal prediction ignoring the covariate shift collapses to 0.416 coverage.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2606.15217 [stat.ML]

(or arXiv:2606.15217v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2606.15217 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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