24 citations · 59 across the 12 of their papers we have counts for
13 papers
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
Thomas M. McDonald, Magnus Ross, Michael T. Smith +1
A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function. The moving average, or process convolutions, construction of GPs…
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…
Adjoint-aided inference of Gaussian process driven differential equations
Paterne Gahungu, Christopher W Lanyon, Mauricio A Alvarez +3
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 lie…
Learning Nonparametric Volterra Kernels with Gaussian Processes
Magnus Ross, Michael T. Smith, Mauricio A. Álvarez
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 proces…
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…
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…
The Limitations of Model Uncertainty in Adversarial Settings
Kathrin Grosse, David Pfaff, Michael Thomas Smith +1
Machine learning models are vulnerable to adversarial examples: minor perturbations to input samples intended to deliberately cause misclassification. While an obvious security thr…
Gaussian Process Regression for Binned Data
Michael Thomas Smith, Mauricio A Alvarez, Neil D Lawrence
Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or…
Killing four birds with one Gaussian process: the relation between different test-time attacks
Kathrin Grosse, Michael T. Smith, Michael Backes
In machine learning (ML) security, attacks like evasion, model stealing or membership inference are generally studied in individually. Previous work has also shown a relationship b…
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models
Kathrin Grosse, David Pfaff, Michael Thomas Smith +1
Machine learning models are vulnerable to Adversarial Examples: minor perturbations to input samples intended to deliberately cause misclassification. Current defenses against adve…
Differentially Private Gaussian Processes
Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence
A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the di…