A Note on How to Remove the $\ln\ln T$ Term from the Squint Bound
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918474636328960 |
|---|---|
| author | Orabona, Francesco |
| author_facet | Orabona, Francesco |
| contents | In Orabona and Pál [2016], we introduced the shifted KT potentials, to remove the $\ln \ln T$ factor in the parameter-free learning with expert bound. In this short technical note, I show that this is equivalent to changing the prior in the Krichevsky--Trofimov algorithm. Then, I show how to use the same idea to remove the $\ln \ln T$ factor in the data-independent bound for the Squint algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26926 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Note on How to Remove the $\ln\ln T$ Term from the Squint Bound Orabona, Francesco Machine Learning Optimization and Control In Orabona and Pál [2016], we introduced the shifted KT potentials, to remove the $\ln \ln T$ factor in the parameter-free learning with expert bound. In this short technical note, I show that this is equivalent to changing the prior in the Krichevsky--Trofimov algorithm. Then, I show how to use the same idea to remove the $\ln \ln T$ factor in the data-independent bound for the Squint algorithm. |
| title | A Note on How to Remove the $\ln\ln T$ Term from the Squint Bound |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2604.26926 |