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Adapting the Linearised Laplace Model Evidence for Modern Deep Learning


Jun 17, 2022
Javier Antorán, David Janz, James Urquhart Allingham, Erik Daxberger, Riccardo Barbano, Eric Nalisnick, José Miguel Hernández-Lobato

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* Paper appearing at ICML 2022 

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Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning


Nov 05, 2021
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi

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* Bayesian Deep Learning Workshop, NeurIPS 2021 

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Laplace Redux -- Effortless Bayesian Deep Learning


Jun 28, 2021
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

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* Source Code: https://github.com/AlexImmer/Laplace; Library Documentation: https://aleximmer.github.io/Laplace/ 

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Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference


Oct 28, 2020
Erik Daxberger, Eric Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

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* 15 pages, extended version with supplementary material 

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Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining


Jun 16, 2020
Austin Tripp, Erik Daxberger, José Miguel Hernández-Lobato

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* 22 pages, 13 figures. Includes supplementary material 

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Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection


Dec 11, 2019
Erik Daxberger, José Miguel Hernández-Lobato

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* 18 pages, extended version with supplementary material 

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Mixed-Variable Bayesian Optimization


Jul 02, 2019
Erik Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause

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* 24 pages, extended version with supplementary material 

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Embedding Models for Episodic Memory


Jun 30, 2018
Yunpu Ma, Volker Tresp, Erik Daxberger

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* 23 pages, 3 figures 

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