On Local Posterior Structure in Deep Ensembles

• URL: http://arxiv.org/abs/2503.13296v1
• Date: Mon, 17 Mar 2025 15:41:39 GMT
• Title: On Local Posterior Structure in Deep Ensembles
• Authors: Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen,
• Abstract summary: Deep ensembles of BNNs (DEs) are known to improve calibration.<n>We find that when the ensembles grow large enough, DEs consistently outperform DE-BNNs on in-distribution data.<n>As a final contribution, we open-source the large pool of trained models to facilitate further research on this topic.
• Score: 5.03927767211033
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Bayesian Neural Networks (BNNs) often improve model calibration and predictive uncertainty quantification compared to point estimators such as maximum-a-posteriori (MAP). Similarly, deep ensembles (DEs) are also known to improve calibration, and therefore, it is natural to hypothesize that deep ensembles of BNNs (DE-BNNs) should provide even further improvements. In this work, we systematically investigate this across a number of datasets, neural network architectures, and BNN approximation methods and surprisingly find that when the ensembles grow large enough, DEs consistently outperform DE-BNNs on in-distribution data. To shine light on this observation, we conduct several sensitivity and ablation studies. Moreover, we show that even though DE-BNNs outperform DEs on out-of-distribution metrics, this comes at the cost of decreased in-distribution performance. As a final contribution, we open-source the large pool of trained models to facilitate further research on this topic.

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