18 citations · 51 across the 17 of their papers we have counts for
18 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 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…
An Analysis of the Johnson-Lindenstrauss Lemma with the Bivariate Gamma Distribution
Jason Bernstein, Alec M. Dunton, Benjamin W. Priest
Probabilistic proofs of the Johnson-Lindenstrauss lemma imply that random projection can reduce the dimension of a data set and approximately preserve pairwise distances. If a dist…
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…
TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata
Trevor Steil, Tahsin Reza, Keita Iwabuchi +3
Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched gra…
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…
Scaling Graph Clustering with Distributed Sketches
Benjamin W. Priest, Alec Dunton, Geoffrey Sanders
The unsupervised learning of community structure, in particular the partitioning vertices into clusters or communities, is a canonical and well-studied problem in exploratory graph…
Quantum Machine Learning using Gaussian Processes with Performant Quantum Kernels
Matthew Otten, Imène R. Goumiri, Benjamin W. Priest +2
Quantum computers have the opportunity to be transformative for a variety of computational tasks. Recently, there have been proposals to use the unsimulatably of large quantum devi…
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels
Imène R. Goumiri, Benjamin W. Priest, Michael D. Schneider
While deep neural networks (DNNs) and Gaussian Processes (GPs) are both popularly utilized to solve problems in reinforcement learning, both approaches feature undesirable drawback…
DegreeSketch: Distributed Cardinality Sketches on Massive Graphs with Applications
Benjamin W. Priest
We present DegreeSketch, a semi-streaming distributed sketch data structure and demonstrate its utility for estimating local neighborhood sizes and local triangle count heavy hitte…