Mauricio A. Álvarez

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

2019 · Research Explorer (The University of Manchester)

This paper is concerned with learning and stochastic control in physical systems that contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parameterized covariance structures. The resulting latent force …

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models

2024 · Research Explorer (The University of Manchester)

Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-stationary processes. DGPs typically rely on local inducing point approximations across intermediate GP layers. Recent advances in DGP inference have shown that incorporating global Fourier …

Deep latent force models: ODE-based process convolutions for Bayesian deep learning

2025 · Machine Learning

Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed to address the problem at hand. We introduce a domain-agnostic model to address …