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Distribution Calibration for Regression

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

Standard

Distribution Calibration for Regression. / Song, Hao; Diethe, Tom; Kull, Meelis; Flach, Peter.

International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA. ed. / Kamalika Chaudhuri; Ruslan Salakhutdinov. Proceedings of Machine Learning Research, 2019. p. 5897-5906 (Proceedings of Machine Learning Research; Vol. 97).

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

Harvard

Song, H, Diethe, T, Kull, M & Flach, P 2019, Distribution Calibration for Regression. in K Chaudhuri & R Salakhutdinov (eds), International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, Proceedings of Machine Learning Research, pp. 5897-5906.

APA

Song, H., Diethe, T., Kull, M., & Flach, P. (2019). Distribution Calibration for Regression. In K. Chaudhuri, & R. Salakhutdinov (Eds.), International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA (pp. 5897-5906). (Proceedings of Machine Learning Research; Vol. 97). Proceedings of Machine Learning Research.

Vancouver

Song H, Diethe T, Kull M, Flach P. Distribution Calibration for Regression. In Chaudhuri K, Salakhutdinov R, editors, International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research. 2019. p. 5897-5906. (Proceedings of Machine Learning Research).

Author

Song, Hao ; Diethe, Tom ; Kull, Meelis ; Flach, Peter. / Distribution Calibration for Regression. International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA. editor / Kamalika Chaudhuri ; Ruslan Salakhutdinov. Proceedings of Machine Learning Research, 2019. pp. 5897-5906 (Proceedings of Machine Learning Research).

Bibtex

@inproceedings{89d18ff34fbb4cd5b018fa6a6c837806,
title = "Distribution Calibration for Regression",
abstract = "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.",
keywords = "stat.ML, cs.AI, cs.LG",
author = "Hao Song and Tom Diethe and Meelis Kull and Peter Flach",
year = "2019",
month = "5",
day = "15",
language = "English",
series = "Proceedings of Machine Learning Research",
publisher = "Proceedings of Machine Learning Research",
pages = "5897--5906",
editor = "Kamalika Chaudhuri and Ruslan Salakhutdinov",
booktitle = "International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Distribution Calibration for Regression

AU - Song, Hao

AU - Diethe, Tom

AU - Kull, Meelis

AU - Flach, Peter

PY - 2019/5/15

Y1 - 2019/5/15

N2 - 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.

AB - 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.

KW - stat.ML

KW - cs.AI

KW - cs.LG

M3 - Conference contribution

T3 - Proceedings of Machine Learning Research

SP - 5897

EP - 5906

BT - International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA

A2 - Chaudhuri, Kamalika

A2 - Salakhutdinov, Ruslan

PB - Proceedings of Machine Learning Research

ER -