Aminian, Gholamali;
Bu, Yuheng;
Wornell, Gregory W;
Rodrigues, Miguel RD;
(2022)
Tighter Expected Generalization Error Bounds via Convexity of Information Measures.
In:
Proceedings of the IEEE International Symposium on Information Theory (ISIT) 2022.
(pp. pp. 2481-2486).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Generalization error bounds are essential to understanding machine learning algorithms. This paper presents novel expected generalization error upper bounds based on the average joint distribution between the output hypothesis and each input training sample. Multiple generalization error upper bounds based on different information measures are provided, including Wasserstein distance, total variation distance, KL divergence, and Jensen-Shannon divergence. Due to the convexity of the information measures, the proposed bounds in terms of Wasserstein distance and total variation distance are shown to be tighter than their counterparts based on individual samples in the literature. An example is provided to demonstrate the tightness of the proposed generalization error bounds.
| Type: | Proceedings paper |
|---|---|
| Title: | Tighter Expected Generalization Error Bounds via Convexity of Information Measures |
| Event: | 2022 IEEE International Symposium on Information Theory (ISIT) |
| Location: | Espoo, Finland |
| Dates: | 26th June - 1st July 2022 |
| ISBN-13: | 978-1-6654-2159-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/isit50566.2022.9834474 |
| Publisher version: | https://doi.org/10.1109/ISIT50566.2022.9834474 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10175497 |
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