21 citations · 42 across the 5 of their papers we have counts for
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stat.ML2018★ 21 cited
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…
stat.ML2018★ 8 cited
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg +4
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) p…