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Probabilistic safety for bayesian neural networks

Abstract:

We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations. Given a compact set of input points, T ⊆ R m, we study the probability w.r.t. the BNN posterior that all the points in T are mapped to the same region S in the output space. In particular, this can be used to evaluate the probability that a network sampled from the BNN is vulnerable to adversarial attacks. We rely on relaxation techniques from non-convex optimization to develop a method fo...

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Publication status:
Accepted
Peer review status:
Peer reviewed

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Publication website:
http://proceedings.mlr.press/v124/wicker20a.html

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
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Name:
European Commission
Grant:
834115
722022
More from this funder
Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M019918/1
Publisher:
Journal of Machine Learning Research
Host title:
Proceedings of theConference on Uncertainty in Artificial Intelligence (UAI 2020)
Series:
Proceedings of Machine Learning Research
Volume:
124
Issue:
2020
Pages:
1198-1207
Publication date:
2020-09-27
Acceptance date:
2020-05-14
Event title:
Conference on Uncertainty in Artificial Intelligence (UAI 2020)
Event location:
Online
Event website:
http://www.auai.org/uai2020/
Event start date:
2020-08-03
Event end date:
2020-08-06
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1114608
Local pid:
pubs:1114608
Deposit date:
2020-06-24

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