arxiv
PublishedOctober 22, 2026 at 4:00 AM
Generalization Bounds via Vector-Valued $L_p$ Correlation $V_\alpha$
Publisher summary· verbatim
arXiv:2610.29425v1 Announce Type: new Abstract: Understanding generalization of randomized learning algorithms remains a central challenge. Mutual-information and total-variation bounds provide complementary ways to quantify data--hypothesis dependence, but they can behave quite differently in high-
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivHow to Use Parallel Data for Multilingual Pretraining5harxivKIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children5harxivBuilding AI Research Teams for Autonomous Discovery of Linear Attention Architectures5harxivCSD: Common-Support Distillation for Cross-Tokenizer Knowledge Transfer5hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗