New research explores computable learning and RER classes

This paper explores computable probably approximately correct (CPAC) learning, focusing on how the fundamental theorem of statistical learning changes when learners must be computable functions. Researchers introduced an effective VC-dimension to recover analogs of the theorem in this setting. The study investigates the relationship between CPAC learning and recursively enumerable representable (RER) classes, demonstrating that effective VC-dimensions can vary widely even for RER classes. The findings also show that CPAC learnability can be characterized by the containment of RER classes that produce the same samples, and that CPAC learnable classes with unique identification properties are necessarily RER. Additionally, the paper establishes that agnostic learnability is achievable for RER classes through the concept of nonuniform CPAC learning. AI

IMPACT This research advances theoretical understanding of computable learning, potentially influencing the design of future AI algorithms that require computational guarantees.

RANK_REASON The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• agnostic learnability
• CPAC learning
• David Kattermann
• effective VC-dimension
• nonuniform CPAC learning
• PAC-Learnability
• RER classes
• Vapnik-Chervonenkis Dimension and (Pseudo-)Hyperplane Arrangements

• paper
• other

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