Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep
learning in safety-critical applications. Despite significant efforts, both practical and …

Physics-informed neural networks (PINNs) have been shown to be effective in solving partial
differential equations by capturing the physics induced constraints as a part of the training …

We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input
perturbations. Given a compact set of input points, $ T\subseteq R^ m $, we study the …

We consider adversarial training of deep neural networks through the lens of Bayesian
learning and present a principled framework for adversarial training of Bayesian Neural …

Two key challenges facing modern deep learning is mitigating deep networks vulnerability
to adversarial attacks, and understanding deep learning's generalization capabilities …

Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …

We present a data-driven framework for strategy synthesis for partially-known switched
stochastic systems. The properties of the system are specified using linear temporal logic …

Neural network NLP models are vulnerable to small modifications of the input that maintain
the original meaning but result in a different prediction. In this paper, we focus on robustness …

We consider the problem of computing reach-avoid probabilities for iterative predictions
made with Bayesian neural network (BNN) models. Specifically, we leverage bound …

Abstract Model-based reinforcement learning seeks to simultaneously learn the dynamics of
an unknown stochastic environment and synthesise an optimal policy for acting in it …