Interactive machine learning from multiple biodata sources

  • Sundin, Iiris (Project Member)
  • Hegde, Pashupati (Project Member)
  • Eranti, Pradeep (Project Member)
  • Kaski, Samuel (Principal investigator)
  • Reinvall, Jaakko (Project Member)
  • Jälkö, Joonas (Project Member)
  • Aushev, Alexander (Project Member)
  • Celikok, Mustafa Mert (Project Member)
  • Kangas, Juho-Kustaa (Project Member)
  • Honkamaa, Joel (Project Member)
  • Afrabandpey, Homayun (Project Member)
  • Daee, Pedram (Project Member)
  • Blomstedt, Paul (Project Member)
  • Chen, Yi (Project Member)
  • Qin, Xiangju (Project Member)
  • Shen, Zheyang (Project Member)
  • Peltola, Tomi (Project Member)
  • Pesonen, Henri (Project Member)
  • Siren, Jukka (Project Member)

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
StatusFinished
Effective start/end date01/01/201631/12/2018

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Bayesian Inference for Optimal Transport with Stochastic Cost

    Mallasto, A., Heinonen, M. & Kaski, S., 2021, Proceedings of Asian Conference on Machine Learning. p. 1601-1616 16 p. (Proceedings of Machine Learning Research; vol. 157).

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

    Open Access
    File
    6 Downloads (Pure)
  • De-randomizing MCMC dynamics with the diffusion Stein operator

    Shen, Z., Heinonen, M. & Kaski, S., 2021, Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021). 11 p. (Advances in Neural Information Processing Systems).

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

    Open Access
    File
    3 Downloads (Pure)
  • Federated Stochastic Gradient Langevin Dynamics

    El Mekkaoui, K., Parente Paiva Mesquita, D., Blomstedt, P. & Kaski, S., Dec 2021, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. p. 1703-1712 10 p. (Proceedings of Machine Learning Research ; no. 161).

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

    Open Access
    File
    3 Downloads (Pure)