Conference item
On PAC-Bayesian reconstruction guarantees for VAEs
- Abstract:
- Despite its wide use and empirical successes, the theoretical understanding and study of the behaviour and performance of the variational autoencoder (VAE) have only emerged in the past few years. We contribute to this recent line of work by analysing the VAE’s reconstruction ability for unseen test data, leveraging arguments from the PAC-Bayes theory. We provide generalisation bounds on the theoretical reconstruction error, and provide insights on the regularisation effect of VAE objectives. We illustrate our theoretical results with supporting experiments on classical benchmark datasets.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.2MB, Terms of use)
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- Publication website:
- https://proceedings.mlr.press/v151/cherief-abdellatif22a.html
Authors
- Publisher:
- Journal of Machine Learning Research
- Host title:
- Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)
- Volume:
- 151
- Pages:
- 3066-3079
- Series:
- Proceedings of Machine Learning Research
- Publication date:
- 2022-09-18
- Event title:
- 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)
- Event location:
- Valencia, Spain
- Event website:
- http://aistats.org/aistats2022/
- Event start date:
- 2022-03-28
- Event end date:
- 2022-03-30
- ISSN:
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2640-3498
- Language:
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English
- Keywords:
- Pubs id:
-
1276300
- Local pid:
-
pubs:1276300
- Deposit date:
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2022-12-08
Terms of use
- Copyright holder:
- Chérief-Abdellatif et al
- Copyright date:
- 2022
- Rights statement:
- © 2022 by the author(s).
- Notes:
- This paper was presented at the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), Valencia, Spain, 28th-30th March 2022.
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