Full Text Available

Note: Clicking the button above will open the full text document at the original institutional repository in a new window.

Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift

Saved in:
Bibliographic Details
Published in:ArXiv cs.DS Recent Papers
Format: Online Article RSS Article
Published: 2026
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1864946161638440960
collection WordPress RSS
FRELIP Feed Integration
container_title ArXiv cs.DS Recent Papers
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:50687
institution FRELIP
journal_source_facet ArXiv cs.DS Recent Papers
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
ArXiv cs.DS Recent Papers
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display ArXiv cs.DS Recent Papers
Computer Science & IT
Engineering & Technology
ArXiv cs.DS Recent Papers
Computer Science & IT
Engineering & Technology
subject_facet ArXiv cs.DS Recent Papers
Computer Science & IT
Engineering & Technology
title Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
title_auth Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
title_full Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
title_fullStr Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
title_full_unstemmed Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
title_short Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
title_sort equivalence of coarse and fine-grained models for learning with distribution shift
topic ArXiv cs.DS Recent Papers
Computer Science & IT
Engineering & Technology
url https://arxiv.org/abs/2605.07005v1