Compositional modeling of nonlinear dynamical systems with ode-based random features
T McDonald, M Álvarez - Advances in neural information …, 2021 - proceedings.neurips.cc
Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also
accurately quantifying uncertainty is a challenging task, which often requires problem …
accurately quantifying uncertainty is a challenging task, which often requires problem …
Joint models for event prediction from time series and survival data
We present a nonparametric prognostic framework for individualized event prediction based
on joint modeling of both time series and time-to-event data. Our approach exploits a …
on joint modeling of both time series and time-to-event data. Our approach exploits a …
Adjoint-aided inference of Gaussian process driven differential equations
Linear systems occur throughout engineering and the sciences, most notably as differential
equations. In many cases the forcing function for the system is unknown, and interest lies in …
equations. In many cases the forcing function for the system is unknown, and interest lies in …
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning
T Baldwin-McDonald, MA Álvarez - arXiv preprint arXiv:2311.14828, 2023 - arxiv.org
Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also
accurately quantifying uncertainty is a challenging task, which often requires problem …
accurately quantifying uncertainty is a challenging task, which often requires problem …
Learning nonparametric Volterra kernels with Gaussian processes
This paper introduces a method for the nonparametric Bayesian learning of nonlinear
operators, through the use of the Volterra series with kernels represented using Gaussian …
operators, through the use of the Volterra series with kernels represented using Gaussian …
Physically-inspired Gaussian process models for post-transcriptional regulation in Drosophila
AF López-Lopera, N Durrande… - IEEE/ACM transactions …, 2019 - ieeexplore.ieee.org
The regulatory process of Drosophila is thoroughly studied for understanding a great variety
of biological principles. While pattern-forming gene networks are analyzed in the …
of biological principles. While pattern-forming gene networks are analyzed in the …
Multioutput convolution spectral mixture for Gaussian processes
K Chen, T van Laarhoven, P Groot… - … on Neural Networks …, 2019 - ieeexplore.ieee.org
Multioutput Gaussian processes (MOGPs) are an extension of Gaussian processes (GPs) for
predicting multiple output variables (also called channels/tasks) simultaneously. In this …
predicting multiple output variables (also called channels/tasks) simultaneously. In this …
Generalized convolution spectral mixture for multitask Gaussian processes
K Chen, T van Laarhoven, P Groot… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Multitask Gaussian processes (MTGPs) are a powerful approach for modeling
dependencies between multiple related tasks or functions for joint regression. Current …
dependencies between multiple related tasks or functions for joint regression. Current …
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
X Shi, T Baldwin-McDonald, MA Álvarez - arXiv preprint arXiv:2407.01856, 2024 - arxiv.org
Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-
stationary processes. DGPs typically rely on local inducing point approximations across …
stationary processes. DGPs typically rely on local inducing point approximations across …
Patient-specific effects of medication using latent force models with Gaussian processes
A multi-output Gaussian process (GP) is a flexible Bayesian nonparametric framework that
has proven useful in jointly modeling the physiological states of patients in medical time …
has proven useful in jointly modeling the physiological states of patients in medical time …