Mario Boley

Mario Boley

Statistical Machine Learning, Optimisation, Applications to Materials Science and Chemistry

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

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portfolio

projects

Human-Simulatable Additive Classification Models

Published:

Generalised additive classification models provide probabilistic predictions by adding the output of individual predictors to form a raw score $a=a_1+\dots+a_k$ and then by mapping this raw score to a probability $p(a)$. This process is considered human-interpretable because of the relative ease with which humans can carry out addition, and this is further enhanced by model fitting methods that restrict the individual predictor output to a small set of round values, i.e., $a_i \in \{-5, \dots, 5\}$. However, even when the resulting raw sum is round and easy to compute, the standard classification machinery based around the logistic transformation $p(a)=e^a/(1+e^a)$ renders it hard for humans to convert raw outputs to probabilities. For instance, a raw model output of $a=1$ corresponds to a probability $e/(1+e) \approx$ 0.7311 and $a=2$ corresponds to $e^2/(1+e^2)\approx 0.8808$.

Graph Neural Networks for Guiding Chemical Synthesis of Metal–Organic Frameworks

Published:

This project will explore how graph neural networks (GNNs) can be used to probabilistically predict successful crystallization conditions in the reticular synthesis of novel metal–organic frameworks (MOFs). MOFs are rich chemical structures with important applications in carbon capture and storage, water harvesting from air, hydrogen storage, and catalysis—areas central to addressing global energy and sustainability challenges.

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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Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly

Published in Journal of Chemical Information and Modeling, 2023

Prodives interpetable models to predict the morphological outcome of polymerization-induced self-assemblies with a performance that suffices to reduce time-consuming experimentations by practitioners.

Recommended citation: Y Lu, D Yalcin, PJ Pigram, LD Blackman, M Boley. "Interpretable machine learning models for phase prediction in polymerization-induced self-assembly." Journal of Chemical Information and Modeling 63, no. 11 (2023): 3288-3306.
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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:

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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teaching

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.