Active learning for decision-making from imbalanced observational data

Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning can help personalized decision support by learning models to predict individual treatment effects (TTE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target unit after observing its covariates x and predicted outcomes p(ỹ

Original languageEnglish (US)
Title of host publication36th International Conference on Machine Learning, ICML 2019
PublisherInternational Machine Learning Society (IMLS)
Pages10578-10587
Number of pages10
ISBN (Electronic)9781510886988
StatePublished - Jan 1 2019
Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
Duration: Jun 9 2019Jun 15 2019

Publication series

Name36th International Conference on Machine Learning, ICML 2019
Volume2019-June

Conference

Conference36th International Conference on Machine Learning, ICML 2019
CountryUnited States
CityLong Beach
Period6/9/196/15/19

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Active learning for decision-making from imbalanced observational data'. Together they form a unique fingerprint.

  • Cite this

    Sundin, I., Schulam, P., Siivola, E., Vehtari, A., Saria, S., & Kaski, S. (2019). Active learning for decision-making from imbalanced observational data. In 36th International Conference on Machine Learning, ICML 2019 (pp. 10578-10587). (36th International Conference on Machine Learning, ICML 2019; Vol. 2019-June). International Machine Learning Society (IMLS).