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PAC learning

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

Papers

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Showing 131–140 of 289 papers

TitleStatusHype
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time—0
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random—0
Learning pseudo-Boolean k-DNF and Submodular Functions—0
Learning Query Inseparable ELH Ontologies—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Learning Time Dependent Choice—0
Is Nash Equilibrium Approximator Learnable?—0
Learning under p-Tampering Attacks—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
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