528 citations · 1.1k across the 28 of their papers we have counts for
5 papers · 1 filter
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…
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob R Gardner +2
Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional…
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…
Simple Black-box Adversarial Attacks
Chuan Guo, Jacob R. Gardner, Yurong You +2
We propose an intriguingly simple method for the construction of adversarial images in the black-box setting. In constrast to the white-box scenario, constructing black-box adversa…
Exact Gaussian Processes on a Million Data Points
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner +3
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedur…