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PAC learning

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

Papers

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Showing 101–110 of 289 papers

TitleStatusHype
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
SAT-Based PAC Learning of Description Logic ConceptsCode0
A Parameterized Theory of PAC Learning—0
Probably Approximately Correct Federated Learning—0
Online Learning and Disambiguations of Partial Concept Classes—0
Lifting uniform learners via distributional decomposition—0
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
On the complexity of PAC learning in Hilbert spaces—0
Do PAC-Learners Learn the Marginal Distribution?—0
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