Time series prediction techniques have been used in many real-world applications such as
financial market prediction, electric utility load forecasting, weather and environmental state …

Modeling the interactions of water and energy systems is important to the enforcement of
infrastructure security and system sustainability. To this end, recent technological …

Most typical statistical and machine learning approaches to time series modeling optimize a
single-step prediction error. In multiple-step simulation, the learned model is iteratively …

Abstract This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear
time series analysis. A GPDM comprises a low-dimensional latent space with associated …

Regressing the vector field of a dynamical system from a finite number of observed states is
a natural way to learn surrogate models for such systems. We present variants of cross …

The paper introduces an action recognition framework that uses concepts from the theory of
chaotic systems to model and analyze nonlinear dynamics of human actions. Trajectories of …

This paper presents a kernelized version of the extended recursive least squares (EX-KRLS)
algorithm which implements for the first time a general linear state model in reproducing …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

This thesis presents extensions of the Gaussian Process (GP) model, based on approximate
methods allowing the model to deal with input uncertainty. Zero-mean GPs with Gaussian …

Motivation: Statistical inference of biological networks such as gene regulatory networks,
signaling pathways and metabolic networks can contribute to build a picture of complex …