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PAC learning

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

Papers

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Showing 231–240 of 289 papers

TitleStatusHype
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Probably Approximately Precision and Recall Learning—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
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
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