New research clarifies optimal scales for machine learning learnability

A new research paper titled "Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale" delves into the optimal scales for real-valued function classes to exhibit uniform convergence and learnability. The study establishes a scale-sensitive generalization of the PAC learning theorem, demonstrating equivalences between uniform convergence, agnostic learnability, and the finiteness of the fat-shattering dimension at specific scales. This work resolves several open questions in machine learning theory, including those posed by Anthony and Bartlett, and Alon et al., by providing precise scales governing learnability and improving existing bounds on metric entropy. AI

IMPACT This research refines theoretical understanding of learnability and evaluability in machine learning, potentially influencing future algorithm design and analysis.

RANK_REASON The cluster contains a single academic paper detailing theoretical advancements in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Alon
• Annals of Mathematics
• Anthony
• Cambridge University Press
• Institute for National Security Studies
• International Conference on Machine Learning
• Journal of the ACM
• Phil Long
• probably approximately correct learning
• Rudelson
• Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
• Tom Waknine
• Vershynin

• paper
• other

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