9 citations · 10 across the 5 of their papers we have counts for
8 papers
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…
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…