3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
: Robust Principal Component Analysis for Exponential Family Distributions
Xiaojun Zheng, Simon Mak, Liyan Xie +1
Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers. These corrupti…
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…
A multistage framework for studying the evolution of jets and high- probes in small collision systems
Abhijit Majumder, Aaron Angerami, Ritu Arora +48
Understanding the modification of jets and high- probes in small systems requires the integration of soft and hard physics. We present recent developments in extending the JET…
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…
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
Tao Tang, Simon Mak, David Dunson
In many areas of science and engineering, computer simulations are widely used as proxies for physical experiments, which can be infeasible or unethical. Such simulations can often…