In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-
parameter tuning tasks. Our results on the Bayesmark benchmark indicate that …

Inspired by the increasing desire to efficiently tune machine learning hyper-parameters, in
this work we rigorously analyse conventional and nonconventional assumptions inherent to …

The porous underground structures have recently attracted researchers' attention for
hydrogen gas storage due to their high storage capacity. One of the challenges in storing …

To enable closed form conditioning, a common assumption in Gaussian process (GP)
regression is independent and identically distributed Gaussian observation noise. This …

Accurate outlier detection is not only a necessary preprocessing step, but can itself give
important insights into the data. However, especially, for non-linear regression the detection …

This paper introduces techniques in Gaussian process regression model for spatio-temporal
data collected from complex systems. This study focuses on extracting local structures and …

State estimation of dynamical systems is crucial for providing new decision-making and
system automation information in different applications. However, the assumptions on the …

The modeling capabilities of a Gaussian Process (GP), such as generalization, nonlinearity,
and smoothness, are largely determined by the choice of its kernel. A popular family of …

Due to its capacity to depict intricate geometric shapes and topological structures, moving
total least squares (MTLS) method has garnered considerable attention from a diverse …

This paper presents a new robust Gaussian process regression (GPR) algorithm based on
identifying and trimming outliers, and it can reduce the computation time and improve the …