Group-realizable multi-group learning by minimizing empirical risk
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915750885720064 |
|---|---|
| author | Ardeshir, Navid Deng, Samuel Hsu, Daniel Liu, Jingwen |
| author_facet | Ardeshir, Navid Deng, Samuel Hsu, Daniel Liu, Jingwen |
| contents | The sample complexity of multi-group learning is shown to improve in the group-realizable setting over the agnostic setting, even when the family of groups is infinite so long as it has finite VC dimension. The improved sample complexity is obtained by empirical risk minimization over the class of group-realizable concepts, which itself could have infinite VC dimension. Implementing this approach is also shown to be computationally intractable, and an alternative approach is suggested based on improper learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16922 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Group-realizable multi-group learning by minimizing empirical risk Ardeshir, Navid Deng, Samuel Hsu, Daniel Liu, Jingwen Machine Learning The sample complexity of multi-group learning is shown to improve in the group-realizable setting over the agnostic setting, even when the family of groups is infinite so long as it has finite VC dimension. The improved sample complexity is obtained by empirical risk minimization over the class of group-realizable concepts, which itself could have infinite VC dimension. Implementing this approach is also shown to be computationally intractable, and an alternative approach is suggested based on improper learning. |
| title | Group-realizable multi-group learning by minimizing empirical risk |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.16922 |