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PAC learning

Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.

Papers

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Showing 61–70 of 289 papers

TitleStatusHype
Differentially Private Learning of Geometric Concepts—0
Differentially Private Release and Learning of Threshold Functions—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise—0
Agnostic Smoothed Online Learning—0
A Distributional-Lifting Theorem for PAC Learning—0
Do PAC-Learners Learn the Marginal Distribution?—0
A Theory of PAC Learnability of Partial Concept Classes—0
Distribution-Specific Agnostic Conditional Classification With Halfspaces—0
Distribution Learning Meets Graph Structure Sampling—0
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