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PAC learning

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

Papers

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Showing 111–120 of 289 papers

TitleStatusHype
Do PAC-Learners Learn the Marginal Distribution?—0
Tree Learning: Optimal Algorithms and Sample Complexity—0
Find a witness or shatter: the landscape of computable PAC learning—0
PAC learning and stabilizing Hedonic Games: towards a unifying approach—0
Optimal lower bounds for Quantum Learning via Information Theory—0
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning—0
Bagging is an Optimal PAC Learner—0
PAC Verification of Statistical Algorithms—0
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes—0
On Proper Learnability between Average- and Worst-case Robustness—0
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