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PAC learning

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

Papers

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Showing 221–230 of 289 papers

TitleStatusHype
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
Predicting with Distributions—0
Predictive PAC Learning and Process Decompositions—0
Privacy-preserving Prediction—0
A Characterization of List Learnability—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
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