528 citations · 4.4k across the 100 of their papers we have counts for
6 papers · 2 filters
Scaling Gaussian Process Regression with Derivatives
David Eriksson, Kun Dong, Eric Hans Lee +2
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a G…
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel +2
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficie…
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov +1
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its…
Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger +1
One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions. Recent advances in inducing point methods…
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov +2
Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple a…
Product Kernel Interpolation for Scalable Gaussian Processes
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu +2
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kern…