48 citations · 153 across the 35 of their papers we have counts for
8 papers · 1 filter
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…
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model
Juan-José Giraldo, Mauricio A. Álvarez
A recent novel extension of multi-output Gaussian processes handles heterogeneous outputs assuming that each output has its own likelihood function. It uses a vector-valued Gaussia…
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…
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…
Adversarial Vulnerability Bounds for Gaussian Process Classification
Michael Thomas Smith, Kathrin Grosse, Michael Backes +1
Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threa…
Multi-task Learning for Aggregated Data using Gaussian Processes
Fariba Yousefi, Michael Thomas Smith, Mauricio A. Álvarez
Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spa…
Black-Box Inference for Non-Linear Latent Force Models
Wil O. C. Ward, Tom Ryder, Dennis Prangle +1
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussia…
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…