Simon Mak

most citedAdditive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma

3 citations · 6 across the 10 of their papers we have counts for

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2 papers · 1 filter

stat.ME2023★ 1 cited

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…

stat.ME2023

$e^{RPCA}$ : 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…

ProSpar-GP: scalable Gaussian process modeling with massive non-stationary datasets

eRPCA: Robust Principal Component Analysis for Exponential Family Distributions

$e^{RPCA}$ : Robust Principal Component Analysis for Exponential Family Distributions