Bayesian leave-one-out cross-validation for large data

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Conference article in proceedings
Date
2019
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en
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10
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Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, Volume 97
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.
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Magnusson , M , Andersen , M , Jonasson , J & Vehtari , A 2019 , Bayesian leave-one-out cross-validation for large data . in 36th International Conference on Machine Learning, ICML 2019 . Proceedings of Machine Learning Research , vol. 97 , JMLR , International Conference on Machine Learning , Long Beach , California , United States , 09/06/2019 . < http://proceedings.mlr.press/v97/magnusson19a.html >