18 citations · 42 across the 13 of their papers we have counts for
13 papers
Correspondence of NNGP Kernel and the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Kernels representing limiting cases of neural network architectures have recently gained popularity. However, the application and performance of these new kernels compared to exist…
Identifiability and Sensitivity Analysis of Kriging Weights for the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Gaussian process (GP) models are effective non-linear models for numerous scientific applications. However, computation of their hyperparameters can be difficult when there is a la…
A Scalable Gaussian Process Approach to Shear Mapping with MuyGPs
Gregory Sallaberry, Benjamin W. Priest, Robert Armstrong +4
Analysis of cosmic shear is an integral part of understanding structure growth across cosmic time, which in-turn provides us with information about the nature of dark energy. Conve…
A Robust Approach to Gaussian Processes Implementation
Juliette Mukangango, Amanda Muyskens, Benjamin W. Priest
Gaussian Process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes…
Enhancing Electrocardiography Data Classification Confidence: A Robust Gaussian Process Approach (MuyGPs)
Ukamaka V. Nnyaba, Hewan M. Shemtaga, David W. Collins +3
Analyzing electrocardiography (ECG) data is essential for diagnosing and monitoring various heart diseases. The clinical adoption of automated methods requires accurate confidence…
Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes
Chinedu Eleh, Yunli Zhang, Rafael Bidese +4
Stellar blends, where two or more stars appear blended in an image, pose a significant visualization challenge in astronomy. Traditionally, distinguishing these blends from single…
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization
Killian Wood, Alec M. Dunton, Amanda Muyskens +1
Gaussian processes (GPs) are Bayesian non-parametric models popular in a variety of applications due to their accuracy and native uncertainty quantification (UQ). Tuning GP hyperpa…
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)
Imène R. Goumiri, Alec M. Dunton, Amanda L. Muyskens +2
Temporal variations of apparent magnitude, called light curves, are observational statistics of interest captured by telescopes over long periods of time. Light curves afford the e…
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation
Alec M. Dunton, Benjamin W. Priest, Amanda Muyskens
Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic co…
Gaussian Process Classification for Galaxy Blend Identification in LSST
James J. Buchanan, Michael D. Schneider, Robert E. Armstrong +3
A significant fraction of observed galaxies in the Rubin Observatory Legacy Survey of Space and Time (LSST) will overlap at least one other galaxy along the same line of sight, in…
Star-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification
Amanda L. Muyskens, Imène R. Goumiri, Benjamin W. Priest +4
We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in h…
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation
Amanda Muyskens, Benjamin Priest, Imène Goumiri +1
Gaussian processes (GPs) are non-linear probabilistic models popular in many applications. However, naïve GP realizations require quadratic memory to store the covariance matrix an…
Star-Galaxy Separation via Gaussian Processes with Model Reduction
Imène R. Goumiri, Amanda L. Muyskens, Michael D. Schneider +2
Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Bein…