Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for …
An Illustrated Guide to the Knowledge Gradient Acquisition Function
A short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.
Last updated on Oct 22, 2022
7 min read
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based method for asynchronous multi-fidelity hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization, achieving substantial speed-ups over current state-of-the-art methods on challenging benchmarks for tabular data, image classification, and language modeling.
A Handbook for Sparse Variational Gaussian Processes
A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.
Last updated on Oct 19, 2022
21 min read