9 citations · 11 across the 9 of their papers we have counts for
4 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…
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…
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…
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…