On the adversarial robustness of Gaussian processes - Patanè, A (2020)
We study the robustness of Bayesian inference with Gaussian processes (GP) under adversarial attack settings. We begin by noticing how the distinction between model prediction and decision in Bayesian settings naturally leads us to give two different notions of adversarial robustness. The former, probabilistic adversarial robustness, is concerned with the behaviour of the posterior distribution, formally characterising its worst-case attack uncertainty. On the other hand, adversarial robustness is concerned with local stability of the model decision, and is strictly correlated with bounds on the predictive posterior distribution of the model.