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Sequential KDE‑guided zero-shot regression under process changes across materials
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DOI：10.1016/j.compchemeng.2025.109522.png)
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Abstract
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• Sequential self-training enables zero-shot regression under domain shift. • KDE score thresholds select source-process samples near target-process samples. • A generator adds pseudo-target-process variables. • A regressor learns from observed data and pseudo-augmented data • Automatic reuse of informative source data reduces the need for new experiments.
Keywords:
Transfer learning
Zero-shot regression
Self-training
Kernel density estimation
Domain generalization
Process changes
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