The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Gaussian process models (GPMs) are widely regarded as a prominent tool for learning
statistical data models that enable interpolation, regression, and classification. These …

The accurate predictions and principled uncertainty measures provided by GP regression
incur $ O (n^ 3) $ cost which is prohibitive for modern-day large-scale applications. This has …

In the era of chemical big data, the high complexity and strong interdependencies present in
the datasets pose considerable challenges when constructing accurate parametric models …

Additive spatial statistical models with weakly stationary process assumptions have become
standard in spatial statistics. However, one disadvantage of such models is the computation …

With the rising complexity of numerous novel applications that serve our modern society
comes the strong need to design efficient computing platforms. Designing efficient hardware …

Gaussian Process Models (GPMs) are widely regarded as a prominent tool for capturing the
inherent characteristics of data. These bayesian machine learning models allow for data …

Target tracking often relies on complex models with non-stationary parameters. Gaussian
process (GP) is a model-free method that can achieve accurate performance. However, the …

Accurate prediction and uncertainty quantification (UQ) of bearings' remaining useful life
(RUL) are essential for the safe operation of critical machinery. Conventional machine …

Bayesian optimization is an effective technique for black-box optimization, but its
applicability is typically limited to low-dimensional and small-budget problems due to the …