Sparse Gaussian processes and various extensions thereof are enabled through inducing points, that simultaneously bottleneck the predictive capacity and act as the main contributor towards model complexity. However, the number of inducing points is generally not associated with uncertainty which prevents us from applying the apparatus of Bayesian reasoning for identifying an appropriate trade-off. In this work we place a point process prior on the inducing points and approximate the associated posterior through stochastic variational inference. By letting the prior encourage a moderate number of inducing points, we enable the model to learn which and how many points to utilise. We experimentally show that fewer inducing points are preferred by the model as the points become less informative, and further demonstrate how the method can be employed in deep Gaussian processes and latent variable modelling.


Probabilistic Selection of Inducing Points in Sparse Gaussian Processes

**Session Chair**: James Cussens
 **ID // Presenter // Live Discussant** 
8 // Tom Ryder // James Cussens 
227 // George De Ath // Alexander Alemi 
389 // Anders Kirk Uhrenholt // Yingzhen Li 
601 // Philip Boeken // Noa Malem-Shinitski

Bayesian Methods

technical paper

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

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UAI 2021

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

Bjoern Sand Jensen

1

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Probabilistic Selection of Inducing Points in Sparse Gaussian Processes

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