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publications

Identifying domains of applicability of machine learning models for materials science

Published in Nature Communications, 2020

Demonstrates how statistical rule learning enables the discovery of trustworthy input ranges of machine learning models for materials properties.

Recommended citation: C Sutton, M Boley, LM Ghringhelli, M Rupp, J Vreeken, M Scheffler. (2020). "Identifying domains of applicability of machine learning models for materials science." Nature Communications. 11(1),4428.
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Better Short than Greedy: Interpretable Models through Optimal Rule Boosting

Published in Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 2021

Improving the accuracy / comprehensibility trade-off of additive rule ensembles via exactly optimising the gradient boosting objective for conjunctive rules.

Recommended citation: M Boley, S Teshuva, P Le Bodic, G Webb. (2021). "Better Short than Greedy: Interpretable Models through Optimal Rule Boosting." SDM.
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Bayes beats cross validation: fast and accurate ridge regression via expectation maximization

Published in Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS), 2023

Presents a novel method for tuning the regularization hyper-parameter, λ, of a ridge regression that is faster to compute than leave-one-out cross-validation (LOOCV) while yielding estimates of the regression parameters of equal, or particularly in the setting of sparse covariates, superior quality to those obtained by minimising the LOOCV risk

Recommended citation: SY Tew, M Boley, DF Schmidt. (2023). "Bayes beats cross validation: fast and accurate ridge regression via expectation maximization." NeurIPS. 36
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Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles

Published in International Conference on Artificial Intelligence and Statistics, 2024

Improving the accuracy / comprehensibility trade-off of rule ensembles and other additive models via proper adaption of boosting with weight correction.

Recommended citation: F Yang, P Le Bodic, M Kamp, M Boley. (2024). "Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles." AISTATS.
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talks

An Introduction to Subgroup Discovery

Published: September 26, 2018

Subgroup discovery (SGD) is a form of local pattern discovery for labeled data that can help find interpretable descriptors from materials-science data obtained by first-principles calculations. In contrast to global modeling algorithms like kernel ridge regression or artificial neural networks, SGD finds local regions in the input space in which a target property takes on an interesting distribution. These local distributions can potentially reflect interesting scientific phenomena that are not represented in standard machine learning models. In this talk, we go over the conceptual basics of SGD, sketch corresponding search algorithms, and show some exemplary applications to materials-science data obtained by first-principles calculations.

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