2024-04-18-batten24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks	https://github.com/benbatten/TightVerificationOfProbabilisticRobustnessInBNNs	We introduce two algorithms for computing tight guarantees on the probabilistic robustness of Bayesian Neural Networks (BNNs). Computing robustness guarantees for BNNs is a significantly more challenging task than verifying the robustness of standard Neural Networks (NNs) because it requires searching the parameters’ space for safe weights. Moreover, tight and complete approaches for the verification of standard NNs, such as those based on Mixed-Integer Linear Programming (MILP), cannot be directly used for the verification of BNNs because of the polynomial terms resulting from the consecutive multiplication of variables encoding the weights. Our algorithms efficiently and effectively search the parameters’ space for safe weights by using iterative expansion and the network’s gradient and can be used with any verification algorithm of choice for BNNs. In addition to proving that our algorithms compute tighter bounds than the SoA, we also evaluate our algorithms against the SoA on standard benchmarks, such as MNIST and CIFAR10, showing that our algorithms compute bounds up to 40% tighter than the SoA.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	batten24a	0	Tight Verification of Probabilistic Robustness in {B}ayesian Neural Networks	4906	4914	4906-4914	4906	false	Batten, Ben and Hosseini, Mehran and Lomuscio, Alessio	given family Ben Batten given family Mehran Hosseini given family Alessio Lomuscio	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/batten24a/batten24a.pdf