Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance function with a finite Fourier series approximation and treat it as a random va...

Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models. Geospatial datasets at a global scale usually suffer from non-stationarity and non-uniformly smooth spatial boundaries. A Gaussian process regression using a non-stationary covariance function has shown promise for this task, as this covarianc...

State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many algorithms have been applied to perform prognostics for SOH estimation, especially data-driven prognostics algorithms supporting uncertainty representation and management. To des...

We present an information theoretic framework for one-class classification, which allows for deriving several new novelty scores. With these scores, we are able to rank samples according to their novelty and to detect outliers not belonging to a learnt data distribution. The key idea of our approach is to measure the impact of a test sample on the previously learnt model. This is carried out in...

We consider the problem of learning skill templates for a parameterized reinforcement learning problem class T. That is, we assume that a task, i.e., an instance of the problem class, is defined by a task parameter vector τ ∈ T ⊆ R n and an associated interpretation. Likewise, a skill is considered as a parameterized policy with parameter vector θ ∈ R m. A parameterized skill [1] is a mapping Θ...

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian p...

Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has been found to provide good performance, yet scales badly with the number of training data. In this paper we compare experimentally three of the leading approaches towards scaling Gaussian processes regression to large data sets: the subset of representers method, the reduced rank approximation, ...

We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary processes using stationary GP priors. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the input space. We show how our approach can be used to model non-stationary process...

In this paper, we present the Gaussian process regression as the predictive model for Quality-of-Service (QoS) attributes in Web service systems. The goal is to predict performance of the execution system expressed as QoS attributes given existing execution system, service repository, and inputs, e.g., streams of requests. In order to evaluate the performance of Gaussian process regression the ...

This paper proposes a statistical nonparametric speech synthesis technique based on a sparse Gaussian process regression (GPR). In our previous study, we proposed GPR-based speech synthesis where each frame of synthesis units is modeled by a regression of Gaussian processes. Preliminary experiments of synthesizing several phones including both vowels and consonants showed a potential of the tec...