Abstract: I will discuss multiple-data-source prediction problems typical of omics-based precision medicine. What is less typical is that some of the data sources are expert users, whose time is costly, changing the problem to active learning or experimental design for prediction. We have addressed this setup as a probabilistic modelling problem, where different types of sources need different modelling assumptions. I will demonstrate that promising results can be achieved in treatment effectiveness prediction tasks in restricted settings, even by explaining human variation with noise models. Richer behaviour requires richer models that draw from cognitive science.

Speaker: Samuel Kaski

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Seminar Room T6, Konemiehentie 2