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arXiv Machine Learning By Xinyang Li, Kevin Stone, Ajit Vikram

JAREX: An Acquisition Function for Multi-Objective Algorithmic Process Characterization

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JAREX is a Bayesian active‑learning acquisition function designed for multi‑objective process characterization in pharmaceutical manufacturing. It treats characterization as a joint boundary‑learning problem, adaptively selecting experiments to delineate the joint pass region where multiple quality thresholds are simultaneously met. Benchmarks show that JAREX outperforms factorial design of experiments, space‑filling designs, and greedy strategies, achieving more accurate and sample‑efficient boundary recovery, and halving the number of experiments needed for batched runs while maintaining high accuracy.

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