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arXiv Machine Learning By Shih-Ni Prim, Kevin R. Quinlan, Paul Hawkins, Jagadeesh Movva, Annie S. Booth

Actively Learning Joint Contours of Multiple Computer Experiments

Read the original on arXiv Machine Learning →

The paper introduces a joint contour location (jCL) method for actively learning input configurations that simultaneously achieve specified responses across multiple computer experiments. By employing two distinct acquisition schemes—one for exploration and one for exploitation—along with a decision rule, the approach balances learning across multiple response surfaces and provides a natural stopping criterion when no solution exists. The method is demonstrated with Gaussian processes, multitask GPs, and deep GPs, outperforming existing single-response contour location strategies and optimization-based alternatives.

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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