8 citations · 19 across the 4 of their papers we have counts for
7 papers · 1 filter
Modular Gaussian Processes for Transfer Learning
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, one…
Recyclable Gaussian Processes
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a di…
Multinomial Sampling for Hierarchical Change-Point Detection
Lorena Romero-Medrano, Pablo Moreno-Muñoz, Antonio Artés-Rodríguez
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower…
Continual Multi-task Gaussian Processes
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We address the problem of continual learning in multi-task Gaussian process (GP) models for handling sequential input-output observations. Our approach extends the existing prior-p…
Continual Learning for Infinite Hierarchical Change-Point Detection
Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is…
Change-Point Detection on Hierarchical Circadian Models
Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to…