New research questions limits of regularization in multiclass learning

This paper explores fundamental questions in statistical learning theory regarding the learnability of prediction problems and the methods for learning them. The research demonstrates that learning cannot always be reduced to proper learning, even when expanding the hypothesis class. It also characterizes the precise requirements for proper learning, showing that sublinear errors on large samples are necessary for some problems. Furthermore, the study reveals limitations of regularization, proving that not all properly learnable classes can be learned by Structural Risk Minimization (SRM) learners or local regularizers. AI

IMPACT Theoretical findings may influence future algorithm design and understanding of AI model capabilities.

RANK_REASON Academic paper detailing theoretical limits of learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• hypothesis class
• Local regularizer
• Multiclass Learning With Partially Corrupted Labels
• Proper learning of k-term DNF formulas from satisfying assignments
• regularization
• statistical learning theory
• Structural Risk Minimization (SRM)

• paper
• other

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