ICML 2019oral43 citations
Bayesian leave-one-out cross-validation for large data
Måns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari
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) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of using approximate inference techniques and probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation for large datasets. We provide both theoretical and empirical results showing good properties for large data.
BibTeX
@InProceedings{pmlr-v97-magnusson19a,
title = {{B}ayesian leave-one-out cross-validation for large data},
author = {Magnusson, M{\aa}ns and Andersen, Michael and Jonasson, Johan and Vehtari, Aki},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4244--4253},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/magnusson19a/magnusson19a.pdf},
url = {https://proceedings.mlr.press/v97/magnusson19a.html},
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) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of using approximate inference techniques and probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation for large datasets. We provide both theoretical and empirical results showing good properties for large data.}
}