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PAC learning

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

Papers

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Showing 121–130 of 289 papers

TitleStatusHype
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—0
Learning Concepts Definable in First-Order Logic with Counting—0
Learning DNF Expressions from Fourier Spectrum—0
Learning from Mixtures of Private and Public Populations—0
Learning Geometric Concepts with Nasty Noise—0
Efficient PAC Learning from the Crowd with Pairwise Comparisons—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
Distribution Learnability and Robustness—0
Distribution-Independent Reliable Learning—0
A Practical Theory of Generalization in Selectivity Learning—0
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