83 citations · 112 across the 5 of their papers we have counts for
5 papers
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations
Astrid Dahl, Edwin V. Bonilla
We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP)…
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
Karl Krauth, Edwin V. Bonilla, Kurt Cutajar +1
We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference;…
Random Feature Expansions for Deep Gaussian Processes
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi +1
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning prob…
Gray-box inference for structured Gaussian process models
Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla +1
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods. Our approach does n…
Discriminative Probabilistic Prototype Learning
Edwin Bonilla, Antonio Robles-Kelly
In this paper we propose a simple yet powerful method for learning representations in supervised learning scenarios where each original input datapoint is described by a set of vec…