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PAC learning

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

Papers

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Showing 221–230 of 289 papers

TitleStatusHype
Proper vs Improper Quantum PAC learning—0
Provable learning of quantum states with graphical models—0
Quantum hardness of learning shallow classical circuits—0
Quantum statistical query learning—0
Query-driven PAC-Learning for Reasoning—0
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem—0
Realizable Learning is All You Need—0
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Reliable Learning of Halfspaces under Gaussian Marginals—0
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees—0
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