Bayesian Leave-One-Out Cross-Validation for Large Data

Michael Riis Andersen, Mans Magnusson, Johan Jonasson, Aki Vehtari

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

5 Downloads (Pure)

Abstract

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO-CV) is a general approach for assessing the generalizability of a model, but unfortunately, LOO-CV does not scale well to large datasets. We propose a combination of using approximate inference techniques and probabilityproportional-to-size-sampling (PPS) for fast LOOCV model evaluation for large data. We provide both theoretical and empirical results showing good properties for large data.
Original languageEnglish
Title of host publicationProceedings of the 36th International Conference on Machine Learning
Number of pages10
Publication date2019
Publication statusPublished - 2019
EventThirty-sixth International Conference on Machine Learning - Long Beach Convention Center, Long Beach, United States
Duration: 10 Jun 201915 Jun 2019

Conference

ConferenceThirty-sixth International Conference on Machine Learning
LocationLong Beach Convention Center
CountryUnited States
CityLong Beach
Period10/06/201915/06/2019

Cite this

Andersen, M. R., Magnusson, M., Jonasson, J., & Vehtari, A. (2019). Bayesian Leave-One-Out Cross-Validation for Large Data. In Proceedings of the 36th International Conference on Machine Learning
Andersen, Michael Riis ; Magnusson, Mans ; Jonasson, Johan ; Vehtari, Aki. / Bayesian Leave-One-Out Cross-Validation for Large Data. Proceedings of the 36th International Conference on Machine Learning. 2019.
@inproceedings{450192a8805849aea97edf854fd23748,
title = "Bayesian Leave-One-Out Cross-Validation for Large Data",
abstract = "Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO-CV) is a general approach for assessing the generalizability of a model, but unfortunately, LOO-CV does not scale well to large datasets. We propose a combination of using approximate inference techniques and probabilityproportional-to-size-sampling (PPS) for fast LOOCV model evaluation for large data. We provide both theoretical and empirical results showing good properties for large data.",
author = "Andersen, {Michael Riis} and Mans Magnusson and Johan Jonasson and Aki Vehtari",
year = "2019",
language = "English",
booktitle = "Proceedings of the 36th International Conference on Machine Learning",

}

Andersen, MR, Magnusson, M, Jonasson, J & Vehtari, A 2019, Bayesian Leave-One-Out Cross-Validation for Large Data. in Proceedings of the 36th International Conference on Machine Learning. Thirty-sixth International Conference on Machine Learning, Long Beach, United States, 10/06/2019.

Bayesian Leave-One-Out Cross-Validation for Large Data. / Andersen, Michael Riis; Magnusson, Mans ; Jonasson, Johan; Vehtari, Aki.

Proceedings of the 36th International Conference on Machine Learning. 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Bayesian Leave-One-Out Cross-Validation for Large Data

AU - Andersen, Michael Riis

AU - Magnusson, Mans

AU - Jonasson, Johan

AU - Vehtari, Aki

PY - 2019

Y1 - 2019

N2 - Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO-CV) is a general approach for assessing the generalizability of a model, but unfortunately, LOO-CV does not scale well to large datasets. We propose a combination of using approximate inference techniques and probabilityproportional-to-size-sampling (PPS) for fast LOOCV model evaluation for large data. We provide both theoretical and empirical results showing good properties for large data.

AB - Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO-CV) is a general approach for assessing the generalizability of a model, but unfortunately, LOO-CV does not scale well to large datasets. We propose a combination of using approximate inference techniques and probabilityproportional-to-size-sampling (PPS) for fast LOOCV model evaluation for large data. We provide both theoretical and empirical results showing good properties for large data.

M3 - Article in proceedings

BT - Proceedings of the 36th International Conference on Machine Learning

ER -

Andersen MR, Magnusson M, Jonasson J, Vehtari A. Bayesian Leave-One-Out Cross-Validation for Large Data. In Proceedings of the 36th International Conference on Machine Learning. 2019