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Distribution Calibration for Regression

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

Original languageEnglish
Title of host publicationInternational Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA
EditorsKamalika Chaudhuri, Ruslan Salakhutdinov
Publisher or commissioning bodyProceedings of Machine Learning Research
Number of pages10
DateAccepted/In press - 24 Apr 2019
DatePublished (current) - 15 May 2019

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)2640-3498


We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding the predicted target value. We introduce the novel concept of distribution calibration, and demonstrate its advantages over the existing definition of quantile calibration. We further propose a post-hoc approach to improving the predictions from previously trained regression models, using multi-output Gaussian Processes with a novel Beta link function. The proposed method is experimentally verified on a set of common regression models and shows improvements for both distribution-level and quantile-level calibration.

    Structured keywords

  • Digital Health

    Research areas

  • stat.ML, cs.AI, cs.LG

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via PMLR at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 1.92 MB, PDF document


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