Conference item
Bayesian inference with certifiable adversarial robustness
- Abstract:
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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 Networks (BNNs) with certifiable guarantees. We rely on techniques from constraint relaxation of non-convex optimisation problems and modify the standard cross-entropy error model to enforce posterior robustness to worst-case perturbations in ϵ−balls around input points. We illustrate how the resulting framework can be co...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Version of record, pdf, 4.0MB)
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- Publication website:
- http://proceedings.mlr.press/v130/wicker21a.html
Authors
Funding
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 130
- Pages:
- 2431-2439
- Publication date:
- 2021-03-18
- Acceptance date:
- 2021-02-22
- Event title:
- 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
- Event location:
- Virtual event
- Event website:
- https://aistats.org/aistats2021/
- Event start date:
- 2021-04-13
- Event end date:
- 2021-04-15
- ISSN:
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2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1164130
- Local pid:
- pubs:1164130
- Deposit date:
- 2021-03-01
Terms of use
- Copyright holder:
- Wicker et al.
- Copyright date:
- 2021
- Rights statement:
- © The Author(s) 2021.
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