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stat.ML2026
Differential Privacy of Gaussian Process Posterior Sampling
Tomasz Maciazek
We study the privacy of releasing functional posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private. Unlike…
stat.ML2026
The Theory and Practice of Highly Scalable Gaussian Process Regression with Nearest Neighbours
Robert Allison, Tomasz Maciazek, Anthony Stephenson
Gaussian process () regression is a widely used non-parametric modeling tool, but its cubic complexity in the training size limits its use on massive data sets. A practical rem…