Personal profile
Short presentation
I work on interpretable machine learning, nonparametric statistics and statistical machine learning methods where I try to understand and develop new ways of how structure in high(er) dimensional data can be exploited to provide both enhanced predictions and/or quantifications of risk as well as interpretation and visualization of predictors and risk factors.
For more information, please visit my website.
Collaborations and top research areas from the last five years
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Replicating and Extending Chain-Ladder via an Age–Period–Cohort Structure on the Claim Development in a Run-Off Triangle
Pittarello, G., Hiabu, M. & Villegas, A. M., 2026, In: North American Actuarial Journal. 30, 1, p. 1-31Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile2 Downloads (Pure) -
Fast Estimation of Partial Dependence Functions using Trees
Liu, J., Steensgaard, T., Wright, M., Pfister, N. & Hiabu, M., 2025, Proceedings of the 42nd International Conference on Machine Learning. PMLR, p. 39496-39534 39 p. (Proceedings of Machine Learning Research, Vol. 267).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile8 Downloads (Pure) -
Identifiability and estimation of the competing risks model under exclusion restrictions
Hiabu, M., Lo, S. M. S. & Wilke, R. A., 2025, In: Statistica Neerlandica. 79, 1, 15 p., e70003.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile9 Downloads (Pure) -
Pure interaction effects unseen by Random Forests
Blum, R., Hiabu, M., Mammen, E. & Meyer, J. T., 2025, In: Computational Statistics and Data Analysis. 212, 14 p., 108237.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile3 Citations (Scopus)9 Downloads (Pure) -
Smooth backfitting for additive hazard rates
Bischofberger, S. M., Hiabu, M., Mammen, E. & Nielsen, J. P., 2025, In: Scandinavian Journal of Statistics. 52, 4, p. 1625-1669Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile22 Downloads (Pure) -
Fairness: plurality, causality, and insurability
Fahrenwaldt, M., Furrer, C., Hiabu, M. E., Huang, F., Jørgensen, F. H., Lindholm, M., Loftus, J., Steffensen, M. & Tsanakas, A., 2024, In: European Actuarial Journal. 14, p. 317–328Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile2 Citations (Scopus)44 Downloads (Pure) -
Local Linear Smoothing in Additive Models as Data Projection
Hiabu, M., Mammen, E. & Meyer, J. T., 2023, Foundations of Modern Statistics - Festschrift in Honor of Vladimir Spokoiny. Belomestny, D., Butucea, C., Mammen, E., Moulines, E., Reiß, M. & Ulyanov, V. V. (eds.). Springer, p. 197-223 27 p. (Springer Proceedings in Mathematics and Statistics, Vol. 425).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
2 Citations (Scopus) -
Unifying local and global model explanations by functional decomposition of low dimensional structures
Hiabu, M., Meyer, J. T. & Wright, M. N., 2023, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. PMLR, p. 7040-7060 (Proceedings of Machine Learning Research, Vol. 206).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile18 Citations (Scopus)23 Downloads (Pure)