Multi-fidelity Batch Active Learning for Gaussian Process Classifiers

• URL: http://arxiv.org/abs/2510.08865v1
• Date: Thu, 09 Oct 2025 23:45:38 GMT
• Title: Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
• Authors: Murray Cutforth, Yiming Yang, Tiffany Fan, Serge Guillas, Eric Darve,
• Abstract summary: This paper introduces Bernoulli Mutual Information (BPMI), a batch active learning algorithm for multi-fidelity GP classifiers.<n>BPMI circumvents the intractability of calculating mutual information in the probability space by employing a first-order Taylor expansion of the link function.<n>In all experiments, BPMI demonstrates superior performance, achieving higher predictive accuracy for a fixed computational budget.
• Score: 35.50383521744249
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Bernoulli Parameter Mutual Information (BPMI), a batch active learning algorithm for multi-fidelity GP classifiers. BPMI circumvents the intractability of calculating mutual information in the probability space by employing a first-order Taylor expansion of the link function. We evaluate BPMI against several baselines on two synthetic test cases and a complex, real-world application involving the simulation of a laser-ignited rocket combustor. In all experiments, BPMI demonstrates superior performance, achieving higher predictive accuracy for a fixed computational budget.

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