143 citations · 191 across the 8 of their papers we have counts for
4 papers · 1 filter
Deep Sigma Point Processes
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs)…
Parametric Gaussian Process Regressors
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
The combination of inducing point methods with stochastic variational inference has enabled approximate Gaussian Process (GP) inference on large datasets. Unfortunately, the result…
Neural Likelihoods for Multi-Output Gaussian Processes
Martin Jankowiak, Jacob Gardner
We construct flexible likelihoods for multi-output Gaussian process models that leverage neural networks as components. We make use of sparse variational inference methods to enabl…
Differentially Private Bayesian Optimization
Matt J. Kusner, Jacob R. Gardner, Roman Garnett +1
Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in…