3 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
ProSpar-GP: scalable Gaussian process modeling with massive non-stationary datasets
Kevin Li, Simon Mak
Gaussian processes (GPs) are a popular class of Bayesian nonparametric models, but its training can be computationally burdensome for massive training datasets. While there has bee…
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression
Kevin Li, Max Balakirsky, Simon Mak
Fourier feature approximations have been successfully applied in the literature for scalable Gaussian Process (GP) regression. In particular, Quadrature Fourier Features (QFF) deri…
Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma
Kevin Li, Simon Mak, J. -F Paquet +1
The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that…
Multivariate Bayesian dynamic modeling for causal prediction
Graham Tierney, Christoph Hellmayr, Greg Barkimer +2
Bayesian forecasting is developed in multivariate time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated un…