Removing systematic errors for exoplanet search via Latent causes

Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl Johann Simon-Gabriel, Jonas Peters

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

    Abstract

    We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.

    Original languageEnglish (US)
    Title of host publication32nd International Conference on Machine Learning, ICML 2015
    EditorsFrancis Bach, David Blei
    PublisherInternational Machine Learning Society (IMLS)
    Pages2208-2216
    Number of pages9
    ISBN (Electronic)9781510810587
    StatePublished - 2015
    Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
    Duration: Jul 6 2015Jul 11 2015

    Publication series

    Name32nd International Conference on Machine Learning, ICML 2015
    Volume3

    Other

    Other32nd International Conference on Machine Learning, ICML 2015
    CountryFrance
    CityLile
    Period7/6/157/11/15

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Science Applications

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