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07/21/2023
Sparse Cholesky factorization by greedy conditional selection
Dense kernel matrices resulting from pairwise evaluations of a kernel fu...
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05/26/2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
For regression tasks, standard Gaussian processes (GPs) provide natural ...
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01/30/2023
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
To achieve scalable and accurate inference for latent Gaussian processes...
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08/15/2022
Locally anisotropic covariance functions on the sphere
Rapid developments in satellite remote-sensing technology have enabled t...
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07/19/2022
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition
We propose an approximation to the forward-filter-backward-sampler (FFBS...
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03/02/2022
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Bayesian optimization is a technique for optimizing black-box target fun...
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02/25/2022
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Gaussian process (GP) regression is a flexible, nonparametric approach t...
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12/29/2021
Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference
Gaussian processes are widely used as priors for unknown functions in st...
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11/26/2021
Nonstationary Spatial Modeling of Massive Global Satellite Data
Earth-observing satellite instruments obtain a massive number of observa...
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10/13/2021
Ordered conditional approximation of Potts models
Potts models, which can be used to analyze dependent observations on a l...
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08/09/2021
Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields
A multivariate distribution can be described by a triangular transport m...
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12/10/2020
Bayesian nonstationary and nonparametric covariance estimation for large spatial data
In spatial statistics, it is often assumed that the spatial field of int...
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06/30/2020
Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering
Spatial statistics often involves Cholesky decomposition of covariance m...
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05/19/2020
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide
Nitrogen dioxide (NO_2) is a primary constituent of traffic-related air ...
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05/01/2020
Scaled Vecchia approximation for fast computer-model emulation
Many scientific phenomena are studied using computer experiments consist...
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04/29/2020
Sparse Cholesky factorization by Kullback-Leibler minimization
We propose to compute a sparse approximate inverse Cholesky factor L of ...
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06/18/2019
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
Generalized Gaussian processes (GGPs) are highly flexible models that co...
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10/09/2018
Multi-resolution filters for massive spatio-temporal data
Spatio-temporal data sets are rapidly growing in size. For example, envi...
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10/08/2018
Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles
People are increasingly concerned with understanding their personal envi...
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05/08/2018
Vecchia approximations of Gaussian-process predictions
Gaussian processes (GPs) are highly flexible function estimators used fo...
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10/24/2017
Multi-resolution approximations of Gaussian processes for large spatial datasets
Gaussian processes are popular and flexible models for spatial, temporal...
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08/21/2017