27 citations · 45 across the 11 of their papers we have counts for
11 papers
Gaussian Process-driven Hidden Markov Models for Early Diagnosis of Infant Gait Anomalies
Luis Torres-Torres F., Jonatan Arias-García, Hernán F. García +2
Gait analysis is critical in the early detection and intervention of motor neurological disorders in infants. Despite its importance, traditional methods often struggle to model th…
Block-Additive Gaussian Processes under Monotonicity Constraints
M. Deronzier, A. F. López-Lopera, F. Bachoc +2
We generalize the additive constrained Gaussian process framework to handle interactions between input variables while enforcing monotonicity constraints everywhere on the input sp…
Error Bounds for a Kernel-Based Constrained Optimal Smoothing Approximation
Laurence Grammont, François Bachoc, Andrés F. López-Lopera
This paper establishes error bounds for the convergence of a piecewise linear approximation of the constrained optimal smoothing problem posed in a reproducing kernel Hilbert space…
May the Noise be with you: Adversarial Training without Adversarial Examples
Ayoub Arous, Andres F Lopez-Lopera, Nael Abu-Ghazaleh +1
In this paper, we investigate the following question: Can we obtain adversarially-trained models without training on adversarial examples? Our intuition is that training a model wi…
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment
A. F. López-Lopera, D. Idier, J. Rohmer +1
Surrogate models are often used to replace costly-to-evaluate complex coastal codes to achieve substantial computational savings. In many of those models, the hydrometeorological f…
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Andrés F. López-Lopera, ST John, Nicolas Durrande
Gaussian process (GP) modulated Cox processes are widely used to model point patterns. Existing approaches require a mapping (link function) between the unconstrained GP and the po…
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande +3
Adding inequality constraints (e.g. boundedness, monotonicity, convexity) into Gaussian processes (GPs) can lead to more realistic stochastic emulators. Due to the truncated Gaussi…
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
Andrés F. López-Lopera, Nicolas Durrande, Mauricio A. Alvarez
The regulatory process of Drosophila is thoroughly studied for understanding a great variety of biological principles. While pattern-forming gene networks are analysed in the trans…
Finite-dimensional Gaussian approximation with linear inequality constraints
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande +1
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the fini…
Sparse Linear Models applied to Power Quality Disturbance Classification
Andrés F. López-Lopera, Mauricio A. Álvarez, Ávaro A. Orozco
Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as…
Switched latent force models for reverse-engineering transcriptional regulation in gene expression data
Andrés F. López-Lopera, Mauricio A. Álvarez
To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The exter…