Search Results for author:
Found 8 papers, 1 papers with code
Modelling calibration uncertainty in networks of environmental sensors
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift.
Differentially Private Regression and Classification with Sparse Gaussian Processes
We experiment with the use of inducing points to provide a sparse approximation and show that these can provide robust differential privacy in outlier areas and at higher dimensions.
Adversarial Vulnerability Bounds for Gaussian Process Classification
To protect against this we devise an adversarial bound (AB) for a Gaussian process classifier, that holds for the entire input domain, bounding the potential for any future adversarial method to cause such misclassification.
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
The Limitations of Model Uncertainty in Adversarial Settings
Machine learning models are vulnerable to adversarial examples: minor perturbations to input samples intended to deliberately cause misclassification.
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models
In this paper, we leverage Gaussian Processes to investigate adversarial examples in the framework of Bayesian inference.
Differentially Private Gaussian Processes
A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals.
