Search Results for author: Markus Kaiser

Found 5 papers, 1 papers with code

Compositional uncertainty in deep Gaussian processes

1 code implementation17 Sep 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.

Bayesian Inference Gaussian Processes +1

Modulating Surrogates for Bayesian Optimization

no code implementations ICML 2020 Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.

Gaussian Processes

Data Association with Gaussian Processes

no code implementations16 Oct 2018 Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality.

Gaussian Processes Variational Inference

Bayesian Alignments of Warped Multi-Output Gaussian Processes

no code implementations NeurIPS 2018 Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field.

Gaussian Processes Time Series

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