Keywords: materials informatics, sequential learning, random forest, uncertainty estimates
TL;DR: Develops a multivariate, correlated prediction interval that accelerates ML-guided experimental design.
Abstract: Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on multiple correlated model outputs as objectives and/or constraints. To better solve these problems, we propose a recalibrated bootstrap method to generate multivariate prediction intervals for bagged models and show that it is well-calibrated. We apply the recalibrated bootstrap to a simulated sequential learning problem with multiple objectives and show that it leads to a marked decrease in the number of iterations required to find a satisfactory candidate. This indicates that the recalibrated bootstrap could be a valuable tool for practitioners using machine learning to optimize systems with multiple competing targets.
Paper Track: Behind the Scenes
Submission Category: AI-Guided Design
Supplementary Material: zip
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