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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
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise—0
Agnostic Multi-Group Active Learning—0
Distribution-Independent Reliable Learning—0
Distribution Learnability and Robustness—0
Distribution Learning Meets Graph Structure Sampling—0
Distribution-Specific Agnostic Conditional Classification With Halfspaces—0
Do PAC-Learners Learn the Marginal Distribution?—0
Active Learning for Contextual Search with Binary Feedbacks—0
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