Prof. Samuel Kaski
Keynote: Probabilistic Modelling With the Experts
I will discuss multiple-data-source prediction and modelling problems arising in a number of fields, for instance in omics-based precision medicine. What is less common is that some of the data sources are experts, whose time is costly, changing the problem to active learning for prediction. We have addressed this setup as a probabilistic modelling problem, where different types of sources need different modelling assumptions, expert user models ultimately drawing from cognitive science. This brings links to other lines of work on interactive intent modelling and likelihood-free inference to infer the user models.
Samuel Kaski is an Academy Professor at Aalto University, Finland, and the Director of the Finnish Center for Artificial Intelligence FCAI.
His research focuses on probabilistic machine learning, meaning probabilistic modelling and Bayesian inference, applied to difficult problems that are interesting and societally important. His work includes the interrelated topics of analysis of multiple data sources, human-in-the-loop machine learning, simulator-based inference (likelihood-free inference with ABC), and privacy-preserving learning.