48 citations · 153 across the 35 of their papers we have counts for
5 papers · 1 filter
Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets
Chunchao Ma, Arthur Leroy, Mauricio Alvarez
Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs. Generally, MOGPs models assume a…
Modelling calibration uncertainty in networks of environmental sensors
Michael Thomas Smith, Magnus Ross, Joel Ssematimba +4
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complic…
Machine Learning for a Low-cost Air Pollution Network
Michael T. Smith, Joel Ssematimba, Mauricio A. Alvarez +1
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentiall…
Differentially Private Regression and Classification with Sparse Gaussian Processes
Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence
A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable priv…
Variational bridge constructs for approximate Gaussian process regression
Wil O C Ward, Mauricio A Álvarez
This paper introduces a method to approximate Gaussian process regression by representing the problem as a stochastic differential equation and using variational inference to appro…