18 citations · 51 across the 17 of their papers we have counts for
3 papers · 1 filter
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…