1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation
Jian Cao, Matthias Katzfuss
We propose a linear-complexity method for sampling from truncated multivariate normal (TMVN) distributions with high fidelity by applying nearest-neighbor approximations to a produ…
Asymptotic properties of Vecchia approximation for Gaussian processes
Myeongjong Kang, Florian Schäfer, Joseph Guinness +1
Vecchia approximation has been widely used to accurately scale Gaussian-process (GP) inference to large datasets, by expressing the joint density as a product of conditional densit…
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…