Name
Title | ||
|---|---|---|
Information-Theoretic Bounds On The Moments Of The Generalization Error Of Learning Algorithms |
Abstract | ||
|---|---|---|
Generalization error bounds are critical to understanding the performance of machine learning models. In this work, building upon a new bound of the expected value of an arbitrary function of the population and empirical risk of a learning algorithm, we offer a more refined analysis of the generalization behaviour of a machine learning models based on a characterization of (bounds) to their generalization error moments. We discuss how the proposed bounds - which also encompass new bounds to the expected generalization error - relate to existing bounds in the literature. We also discuss how the proposed generalization error moment bounds can be used to construct new generalization error high-probability bounds. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISIT45174.2021.9518043 | 2021 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT) |
Keywords | DocType | Citations |
Population Risk, Empirical Risk, Generalization Error, Generalization Error Moments, Information Measures | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gholamali Aminian | 1 | 0 | 1.35 |
| Laura Toni | 2 | 186 | 15.22 |
| Miguel R. D. Rodrigues | 3 | 1500 | 111.23 |