3 papers
stat.ME2026
Tensor Covariance Estimation via Kronecker-Structured Sparse Inverse Cholesky
Wentao Zhan, Matthias Katzfuss
High-dimensional multi-way (tensor) data pose significant challenges for covariance estimation due to the curse of dimensionality. We introduce a unified framework for scalable est…
stat.ML2026
Scalable Derivative Gaussian Processes via Exact Gradient Reduction
Hyunseok Seung, Matthias Katzfuss
Gradient observations can substantially improve Gaussian process (GP) surrogates, particularly in high-dimensional settings where function evaluations are expensive. However, exact…
stat.CO2026
Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities
Jian Cao, Matthias Katzfuss
Multivariate normal (MVN) probabilities arise in myriad applications, but they are analytically intractable and need to be evaluated via Monte-Carlo-based numerical integration. Fo…