4 papers
Differential Privacy of Gaussian Process Posterior Sampling
Tomasz Maciazek
We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private. Unlike standard di…
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…
Solving one-body ensemble N-representability problems with spin
Julia Liebert, Federico Castillo, Jean-Philippe Labbé +2
The Pauli exclusion principle is fundamental to understanding electronic quantum systems. It namely constrains the expected occupancies of orbitals according to $0 \le…
A toolbox of spin-adapted generalized Pauli constraints
Julia Liebert, Yannick Lemke, Murat Altunbulak +3
We establish a toolbox for studying and applying spin-adapted generalized Pauli constraints (GPCs) in few-electron quantum systems. By exploiting the spin symmetry of realistic …