@inbook{33de018496fd4d2bbeca4685c4ec948b,
title = "Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data",
abstract = "Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets. Although these methods work well for estimating predictive performance for individual models, they are less powerful in model comparison. We propose an efficient method for estimating differences in predictive performance by combining fast approximate LOO surrogates with exact LOO subsampling using the difference estimator and supply proofs with regards to scaling characteristics. The resulting approach can be orders of magnitude more efficient than previous approaches, as well as being better suited to model comparison.",
author = "M{\aa}ns Magnusson and Andersen, {Michael Riis} and Johan Jonasson and Aki Vehtari",
year = "2020",
language = "English",
volume = "108",
series = "Proceedings of Machine Learning Research",
publisher = "International Machine Learning Society (IMLS)",
pages = "341--350",
booktitle = "Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics",
note = "23<sup>rd</sup> International Conference on Artificial Intelligence and Statistics, AISTATS 2020 ; Conference date: 26-08-2020 Through 28-08-2020",
}