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PAC learning

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

Papers

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Showing 191–200 of 289 papers

TitleStatusHype
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Learning from Mixtures of Private and Public Populations—0
A Computational Separation between Private Learning and Online Learning—0
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks—0
An Optimal Elimination Algorithm for Learning a Best Arm—0
Learning Halfspaces with Tsybakov Noise—0
List Learning with Attribute Noise—0
Faster PAC Learning and Smaller Coresets via Smoothed Analysis—0
Probably Approximately Correct Constrained Learning—0
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