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PAC learning

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

Papers

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Showing 11–20 of 289 papers

TitleStatusHype
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds—0
Adversarial Robustness: What fools you makes you stronger—0
Agnostic Learning by Refuting—0
Agnostic Learning of a Single Neuron with Gradient Descent—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
Agnostic Multi-Group Active Learning—0
Agnostic Smoothed Online Learning—0
AI Reasoning Systems: PAC and Applied Methods—0
A learning problem that is independent of the set theory ZFC axioms—0
Active Learning for Contextual Search with Binary Feedbacks—0
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