142 citations · 600 across the 47 of their papers we have counts for
4 papers · 1 filter
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…
Mini-Batch Spectral Clustering
Yufei Han, Maurizio Filippone
The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability,…
Preconditioning Kernel Matrices
Kurt Cutajar, Michael A. Osborne, John P. Cunningham +1
The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue i…