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PAC learning

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

Papers

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Showing 51–60 of 289 papers

TitleStatusHype
Revisiting Agnostic PAC Learning—0
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem—0
Superconstant Inapproximability of Decision Tree Learning—0
Distribution Learnability and Robustness—0
Credit Attribution and Stable Compression—0
Fast Rates for Bandit PAC Multiclass Classification—0
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?—0
On the Computability of Robust PAC Learning—0
Optimistic Rates for Learning from Label ProportionsCode0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
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