Mario Boley

Mario Boley

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

Publications

Some selected publications below. A curated full though probably outdated list can be found in my CV. The most recent list can be found in my Google Scholar profile.

Journal Articles

  1. Rong, Z., Chen, Z., Luong, F. et al. (2025) "Algorithmic iterative reticular synthesis of zeolitic imidazolate framework crystals" Nat. Synth.

  2. Zhao, Y., Tsuchiya, N., Boley, M. et al. "Cortical connectivity, local dynamics and stability correlates of global conscious states". Commun Biol 8 (2025): 1391

  3. 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.

  4. 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.

Conference Papers

  1. Yang, Fan, Pierre Le Bodic, and Mario Boley. "Gradient Boosting Versus Mixed Integer Programming for Sparse Additive Modeling." Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD 2025): 453-470.

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

  3. SY Tew, M Boley, DF Schmidt. (2023). "Bayes beats cross validation: fast and accurate ridge regression via expectation maximization." NeurIPS. 36

  4. Petzka, Henning, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. "Relative flatness and generalization." Advances in neural information processing systems 34 (NeurIPS 2021): 18420-18432.

  5. M Boley, S Teshuva, P Le Bodic, G Webb. (2021). "Better Short than Greedy: Interpretable Models through Optimal Rule Boosting." SDM.