4 citations · 5 across the 2 of their papers we have counts for
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stat.ML2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Jakob Lindinger, David Reeb, Christoph Lippert +1
Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predict…
stat.ML2018
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
David Reeb, Andreas Doerr, Sebastian Gerwinn +1
Gaussian Processes (GPs) are a generic modelling tool for supervised learning. While they have been successfully applied on large datasets, their use in safety-critical application…