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PAC learning

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

Papers

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Showing 21–30 of 289 papers

TitleStatusHype
PAC Learning is just Bipartite Matching (Sort of)—0
Distribution-Specific Agnostic Conditional Classification With Halfspaces—0
Ehrenfeucht-Haussler Rank and Chain of Thought—0
The working principles of model-based GAs fall within the PAC framework: A mathematical theory of problem decomposition—0
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random—0
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise—0
Identifying Information from Observations with Uncertainty and Novelty—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
Monotonic Learning in the PAC Framework: A New Perspective—0
Ensuring superior learning outcomes and data security for authorized learner—0
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