Deep Kernel Learning (DKL) combines the representational power of neural...
We study the problem of certifying the robustness of Bayesian neural net...
In this paper, we introduce BNN-DP, an efficient algorithmic framework f...
We study Individual Fairness (IF) for Bayesian neural networks (BNNs).
S...
Interval Markov Decision Processes (IMDPs) are uncertain Markov models, ...
Vulnerability to adversarial attacks is one of the principal hurdles to ...
Neural Networks (NNs) have been successfully employed to represent the s...
Providing non-trivial certificates of safety for non-linear stochastic
s...
We consider the problem of certifying the individual fairness (IF) of
fe...
Leveraging autonomous systems in safety-critical scenarios requires veri...
We consider the problem of computing reach-avoid probabilities for itera...
Gaussian processes (GPs) enable principled computation of model uncertai...
We consider adversarial training of deep neural networks through the len...
The existence of adversarial examples underscores the importance of
unde...
Neural network NLP models are vulnerable to small modifications of the i...
We study probabilistic safety for Bayesian Neural Networks (BNNs) under
...
Vulnerability to adversarial attacks is one of the principal hurdles to ...
Gaussian Processes (GPs) are widely employed in control and learning bec...
Deep neural network controllers for autonomous driving have recently
ben...
We consider Bayesian classification with Gaussian processes (GPs) and de...
We introduce a probabilistic robustness measure for Bayesian Neural Netw...
Bayesian inference and Gaussian processes are widely used in application...
We consider probabilistic model checking for continuous-time Markov chai...
Both experimental and computational biology is becoming increasingly
aut...