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PAC learning

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

Papers

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Showing 271–280 of 289 papers

Title	Date	Tasks	Status	Hype
Differentially Private Release and Learning of Threshold Functions	Apr 28, 2015	PAC learning	—Unverified	0
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions	Apr 13, 2015	Combinatorial OptimizationPAC learning	—Unverified	0
The VC-Dimension of Similarity Hypotheses Spaces	Feb 25, 2015	PAC learning	—Unverified	0
PAC Learning, VC Dimension, and the Arithmetic Hierarchy	Jun 4, 2014	AllPAC learning	—Unverified	0
Online Learning of k-CNF Boolean Functions	Mar 26, 2014	PAC learning	—Unverified	0
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity	Feb 25, 2014	PAC learning	—Unverified	0
Distribution-Independent Reliable Learning	Feb 20, 2014	AttributePAC learning	—Unverified	0
Characterizing the Sample Complexity of Private Learners	Feb 10, 2014	PAC learning	—Unverified	0
More data speeds up training time in learning halfspaces over sparse vectors	Nov 10, 2013	PAC learning	—Unverified	0
Predictive PAC Learning and Process Decompositions	Sep 19, 2013	PAC learning	—Unverified	0

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