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Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
06/24/2020 ∙ by Florian Wenzel, et al. ∙
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Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
We propose automated augmented conjugate inference, a new inference meth...
02/26/2020 ∙ by Theo Galy-Fajou, et al. ∙
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How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
02/06/2020 ∙ by Florian Wenzel, et al. ∙
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Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multi-class Gaussian process classification ap...
05/23/2019 ∙ by Theo Galy-Fajou, et al. ∙
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Quasi-Monte Carlo Variational Inference
Many machine learning problems involve Monte Carlo gradient estimators. ...
07/04/2018 ∙ by Alexander Buchholz, et al. ∙
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Scalable Generalized Dynamic Topic Models
Dynamic topic models (DTMs) model the evolution of prevalent themes in l...
03/21/2018 ∙ by Patrick Jähnichen, et al. ∙
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Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose an efficient stochastic variational approach to GP classifica...
02/18/2018 ∙ by Florian Wenzel, et al. ∙
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Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector...
07/18/2017 ∙ by Florian Wenzel, et al. ∙
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