Conference item
Chained generalisation bounds
- Abstract:
-
This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique. By developing a general theoretical framework, we establish a duality between generalisation bounds based on the regularity of the loss function, and their chained counterparts, which can be obtained by lifting the regularity assumption from the loss onto its gradient. This allows us to re-derive the chaining mutual information bound from t...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Version of record, pdf, 527.2KB)
-
- Publication website:
- https://proceedings.mlr.press/v178/clerico22a.html
Authors
Bibliographic Details
- Publisher:
- Proceedings of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 178
- Pages:
- 4212-4257
- Publication date:
- 2022-06-28
- Acceptance date:
- 2022-05-07
- Event title:
- 35th Annual Conference on Learning Theory (COLT 2022)
- Event location:
- London
- Event website:
- http://learningtheory.org/colt2022/
- Event start date:
- 2022-07-02
- Event end date:
- 2022-07-05
- ISSN:
-
2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1282361
- Local pid:
- pubs:1282361
- Deposit date:
- 2022-10-11
Terms of use
- Copyright holder:
- Clerico et al.
- Copyright date:
- 2022
- Rights statement:
- © 2022 E. Clerico, A. Shidani, G. Deligiannidis & A. Doucet.
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record