Research

Below is a list of recent papers representative of on-going research themes. You can find a complete list of my publications on my Google Scholar profile or in my CV.

Transfer learning

My most recent work focuses on the development of theory and methods for increasing the robustness of probabilistic machine learning methods to distribution shift.

Sloman, S.J., Martinelli, J., & Kaski, S. (2025). Proxy-informed Bayesian transfer learning with unknown sources. arXiv preprint. doi:10.48550/arXiv.2411.03263

Bayesian experimental design

My work has attempted to understand the role of misspecification in Bayesian experimental design.

Sloman, S.J., Cavagnaro, D.R., & Broomell, S.B. (2024). Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization. Behavior Research Methods. doi:10.3758/s13428-024-02410-7

Sloman, S.J., Bharti, A., Martinelli, J., & Kaski, S. (2024). Bayesian Active Learning in the Presence of Nuisance Parameters. 40th conference on Uncertainty in Artificial Intelligence [oral presentation]. url

Sloman, S.J., Oppenheimer, D.M., Broomell, S.B., & Shalizi, C.R. (2022). Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias. arXiv preprint. doi:10.48550/arXiv.2205.13698

Model parsimony

Machine learning has achieved remarkable success with overparameterised models. This work explores the implications of this for scientific theory and practice, especially in contexts in which "simpler" models have traditionally been preferred.

Dubova, M., Chandramouli, S., Gigerenzer, G., Grünwald, P., Holmes, W., Lombrozo, T., Marelli, M., Musslick, S., Nicenboim, B., Ross, L., Shiffrin, R., White, M., Wagenmakers, E-J.*, Bürkner, P-C.*, Sloman, S.J.* (*joint senior authors) (2025). Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony. PNAS. doi:10.1073/pnas.2401230121

Dubova, M. & Sloman, S.J. (2023). Excess Capacity Learning. Proceedings of the 45th Annual Meeting of the Cognitive Science Society. url