8 citations · 23 across the 18 of their papers we have counts for
5 papers · 1 filter
Challenges of Adversarial Image Augmentations
Arno Blaas, Xavier Suau, Jason Ramapuram +2
Image augmentations applied during training are crucial for the generalization performance of image classifiers. Therefore, a large body of research has focused on finding the opti…
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Xingchen Wan, Henry Kenlay, Binxin Ru +3
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majorit…
On Invariance Penalties for Risk Minimization
Kia Khezeli, Arno Blaas, Frank Soboczenski +2
The Invariant Risk Minimization (IRM) principle was first proposed by Arjovsky et al. [2019] to address the domain generalization problem by leveraging data heterogeneity from diff…
Adversarial Robustness Guarantees for Gaussian Processes
Andrea Patane, Arno Blaas, Luca Laurenti +3
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications. Such scenarios demand that GP decisions are no…
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks
Arno Blaas, Stephen J. Roberts
It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for Bayesian models, as they are well-suited…