Post-hoc explanation methods are used with the intent of providing insights about neural
networks and are sometimes said to help engender trust in their outputs. However, popular …

We introduce two algorithms for computing tight guarantees on the probabilistic robustness
of Bayesian Neural Networks (BNNs). Computing robustness guarantees for BNNs is a …

The application of machine learning in safety-critical systems requires a reliable assessment
of uncertainty. However, deep neural networks are known to produce highly overconfident …

The application of machine learning in safety-critical systems requires a reliable assessment
of uncertainty. However, deep neural networks are known to produce highly overconfident …

Most work on the formal verification of neural networks has focused on bounding the set of
outputs that correspond to a given set of inputs (for example, bounded perturbations of a …

The softmax function is a ubiquitous component at the output of neural networks and
increasingly in intermediate layers as well. This paper provides convex lower bounds and …

Neural network verification aims to provide provable bounds for the output of a neural
network for a given input range. Notable prior works in this domain have either generated …

In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of
adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input …

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 …

We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to
adversarial input perturbations. Specifically, we define two notions of robustness for BNNs in …