Withdrawn arXiv paper links metric entropy to RKBS embeddability

A research paper, recently withdrawn by its author Yiping Lu, explored the relationship between metric entropy and the embeddability of function spaces into reproducing kernel Banach spaces (RKBS). The study established a novel connection, demonstrating that a bound on a function space's metric entropy growth allows its embedding into an $L_p$-type RKBS. This finding suggests that $L_p$-type RKBS offer a broad framework for modeling learnable function classes with controlled metric entropies, potentially illuminating the capabilities and limitations of kernel methods in learning complex function spaces. AI

IMPACT This research explores theoretical underpinnings of kernel methods, potentially influencing future developments in machine learning model design.

RANK_REASON Research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

• function spaces
• kernel methods
• $L_p$-type Reproducing Kernel Banach Space
• Reproducing Kernel Hilbert Space
• Yiping Lu

• paper
• other

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