arXiv Machine Learning
Jul 27

gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points

arXiv:2512. 06143v2 Announce Type: replace Abstract: Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and customizability persists.

By Marcus M. Noack, Mark D. Risser, Hengrui Luo, Vardaan Tekriwal, Ronald J. Pandolfi
arXiv Machine Learning
3d ago

Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models

The paper introduces Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models, enabling the variational posterior over latent variables to depend on GP inducing points. This approach addresses limitations of mean‑field approximations and yields improved reconstruction metrics for data manifold points.

By Maksym Tretiakov, Sarah Lucie Filipp, Vincent Fortuin, Ruth Misener, Ruby Sedgwick, James Odgers
arXiv Machine Learning
3d ago

Predictively Oriented Gaussian Process Posteriors

arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitione...

By Callum Lau, Jeremias Knoblauch, Louis Sharrock