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PAC learning

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

Papers

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Showing 211–220 of 289 papers

TitleStatusHype
Privacy-preserving Prediction—0
Private Hypothesis Selection—0
Private learning implies quantum stability—0
Private PAC learning implies finite Littlestone dimension—0
Private PAC Learning May be Harder than Online Learning—0
On Proper Learnability between Average- and Worst-case Robustness—0
Probably Approximately Correct Constrained Learning—0
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Probably Approximately Precision and Recall Learning—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
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