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PAC learning

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

Papers

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Showing 111–120 of 289 papers

TitleStatusHype
Distribution Learning Meets Graph Structure Sampling—0
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?—0
Is Out-of-Distribution Detection Learnable?—0
Is Transductive Learning Equivalent to PAC Learning?—0
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning—0
Learnability can be undecidable—0
Transductive Learning Is Compact—0
Learnability with PAC Semantics for Multi-agent Beliefs—0
Learnable: Theory vs Applications—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
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