Abstract
We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes.
| Original language | English |
|---|---|
| Pages (from-to) | 489-505 |
| Number of pages | 17 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 98 |
| State | Published - 1 Jan 2019 |
| Event | 30th International Conference on Algorithmic Learning Theory, ALT 2019 - Chicago, United States Duration: 22 Mar 2019 → 24 Mar 2019 |
Keywords
- Agnostic Learning
- Compression Schemes
- Lower Bounds
- Uniform Convergence
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability