We present the first treatment of the arc length of the Gaussian Process (gp) with more than a single output dimension. Gps are commonly used for tasks such as trajectory modelling, where path lengthâ€¦ (More)

Model interpretability is a problem of knowledge extraction from the patterns found in raw data. One key source of knowledge is information visualization, which can help us to gain insights into aâ€¦ (More)

Time-dependent natural phenomena and artificial processes can often be quantitatively expressed as multivariate time series (MTS). As in any other process of knowledge extraction from data, theâ€¦ (More)

An increasing awareness of energy efficiency has led to the development of several improved converter topologies, semiconductor devices and control schemes for distributed energy resources, and,â€¦ (More)

Gradient-based optimization and Markov Chain Monte Carlo sampling can be found at the heart of several machine learning methods. In highdimensional settings, well-known issues such as slow-mixing,â€¦ (More)

We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution over the naturalâ€¦ (More)

In kernel-based machines, the integration of a number of different kernels to build more flexible learning methods is a promising avenue for research. In multiple kernel learning, a compound kernelâ€¦ (More)

Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case inâ€¦ (More)