189 citations · 763 across the 33 of their papers we have counts for
34 papers
Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
Carrie J. Lei-Cramer, Jian Cao, Matthias Katzfuss
Generative modeling of spatio-temporal fields is crucial for a variety of applications, including stochastic weather generators and climate-model surrogates. However, many such fie…
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks
Felix Jimenez, Matthias Katzfuss
Deterministic uncertainty quantification (UQ) in deep learning aims to estimate uncertainty with a single pass through a network by leveraging outputs from the network's feature ex…
Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
Anirban Chakraborty, Matthias Katzfuss
Many applications, including climate-model analysis and stochastic weather generators, require learning or emulating the distribution of a high-dimensional and non-Gaussian spatial…
Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields
Paul F. V. Wiemann, Matthias Katzfuss
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the…
Sparse inverse Cholesky factorization of dense kernel matrices by greedy conditional selection
Stephen Huan, Joseph Guinness, Matthias Katzfuss +2
Dense kernel matrices resulting from pairwise evaluations of a kernel function arise naturally in machine learning and statistics. Previous work in constructing sparse approximate…
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
Felix Jimenez, Matthias Katzfuss
For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification (UQ), while deep neural networks (DNNs) excel at representation learning. Determi…
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
Jian Cao, Myeongjong Kang, Felix Jimenez +3
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matric…
Locally anisotropic covariance functions on the sphere
Jian Cao, Jingjie Zhang, Zhuoer Sun +1
Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that…
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition
Marcin Jurek, Matthias Katzfuss
We propose an approximation to the forward-filter-backward-sampler (FFBS) algorithm for large-scale spatio-temporal smoothing. FFBS is commonly used in Bayesian statistics when wor…
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Felix Jimenez, Matthias Katzfuss
Bayesian optimization is a technique for optimizing black-box target functions. At the core of Bayesian optimization is a surrogate model that predicts the output of the target fun…
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton +1
Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covari…
Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference
Myeongjong Kang, Matthias Katzfuss
Gaussian processes are widely used as priors for unknown functions in statistics and machine learning. To achieve computationally feasible inference for large datasets, a popular a…
Nonstationary Spatial Modeling of Massive Global Satellite Data
Huang Huang, Lewis R. Blake, Matthias Katzfuss +1
Earth-observing satellite instruments obtain a massive number of observations every day. For example, tens of millions of sea surface temperature (SST) observations on a global sca…
Ordered conditional approximation of Potts models
Anirban Chakraborty, Matthias Katzfuss, Joseph Guinness
Potts models, which can be used to analyze dependent observations on a lattice, have seen widespread application in a variety of areas, including statistical mechanics, neuroscienc…
Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields
Matthias Katzfuss, Florian Schäfer
A multivariate distribution can be described by a triangular transport map from the target distribution to a simple reference distribution. We propose Bayesian nonparametric infere…
Bayesian nonstationary and nonparametric covariance estimation for large spatial data
Brian Kidd, Matthias Katzfuss
In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropria…
Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering
Marcin Jurek, Matthias Katzfuss
Spatial statistics often involves Cholesky decomposition of covariance matrices. To ensure scalability to high dimensions, several recent approximations have assumed a sparse Chole…
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide
Kyle P Messier, Matthias Katzfuss
Nitrogen dioxide (NO) is a primary constituent of traffic-related air pollution and has well established harmful environmental and human-health impacts. Knowledge of the spatio…
Scaled Vecchia approximation for fast computer-model emulation
Matthias Katzfuss, Joseph Guinness, Earl Lawrence
Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a po…
Sparse Cholesky factorization by Kullback-Leibler minimization
Florian Schäfer, Matthias Katzfuss, Houman Owhadi
We propose to compute a sparse approximate inverse Cholesky factor of a dense covariance matrix by minimizing the Kullback-Leibler divergence between the Gaussian distribut…
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
Daniel Zilber, Matthias Katzfuss
Generalized Gaussian processes (GGPs) are highly flexible models that combine latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can be used in…
Multi-resolution filters for massive spatio-temporal data
Marcin Jurek, Matthias Katzfuss
Spatio-temporal data sets are rapidly growing in size. For example, environmental variables are measured with ever-higher resolution by increasing numbers of automated sensors moun…
Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles
Yawen Guan, Margaret Johnson, Matthias Katzfuss +4
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their…
Vecchia approximations of Gaussian-process predictions
Matthias Katzfuss, Joseph Guinness, Wenlong Gong +1
Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasib…