Corpus ID: 202583392

Compositional uncertainty in deep Gaussian processes

@inproceedings{Ustyuzhaninov2020CompositionalUI,
  title={Compositional uncertainty in deep Gaussian processes},
  author={Ivan Ustyuzhaninov and Ieva Kazlauskaite and M. Kaiser and Erik Bodin and N. Campbell and C. Ek},
  booktitle={UAI},
  year={2020}
}

• Ivan Ustyuzhaninov, Ieva Kazlauskaite, +3 authors C. Ek
• Published in UAI 2020
• Computer Science, Mathematics

Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations. However, exact Bayesian inference is intractable for DGPs, motivating the use of various approximations. We show that the application of simplifying mean-field… Expand

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed