Interactive machine learning from multiple biodata sources

Project Details

Description

Precision medicine, which is tailored to individual patients, brings a difficult challenge to data analysis methods: The goal is to learn to predict effectiveness of treatments using data with extremely small, even equal to one patient, sample size but an extremely large number of variables. Additional data exist but their relevance is unknown, access may be restricted due to privacy, and the complex models required for good predictivity are computationally heavy. Prof. Kaski develops computational, so-called machine learning methods for analysing multiple data sources, taking into account tacit knowledge of domain experts with interactive modelling. Along with the increasing data-intensivity, similar problems have become very general in other fields of science and services, starting from information retrieval.
Short titleKaski Samuel AP-kulut II
StatusActive
Effective start/end date01/01/201931/08/2021
  • Decision Rule Elicitation for Domain Adaptation

    Nikitin, A. & Kaski, S., 14 Apr 2021, 26th International Conference on Intelligent User Interfaces, IUI 2021. Association for Computing Machinery (ACM), p. 244-248 5 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  • Likelihood-free inference by ratio estimation

    Thomas, O. M., Dutta, R., Corander, J., Kaski, S. & Gutmann, M., 2021, (E-pub ahead of print) In: Bayesian Analysis. 2021, 31 p.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    File
    8 Downloads (Pure)
  • A high-performance implementation of bayesian matrix factorization with limited communication

    Vander Aa, T., Qin, X., Blomstedt, P., Wuyts, R., Verachtert, W. & Kaski, S., 1 Jan 2020, Computational Science – ICCS 2020 - 20th International Conference, Proceedings. Krzhizhanovskaya, V. V., Závodszky, G., Lees, M. H., Sloot, P. M. A., Sloot, P. M. A., Sloot, P. M. A., Dongarra, J. J., Brissos, S. & Teixeira, J. (eds.). p. 3-16 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12142 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access