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PAC learning

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

Papers

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Showing 191–200 of 289 papers

TitleStatusHype
PAC Generalization via Invariant Representations—0
PAC learning and stabilizing Hedonic Games: towards a unifying approach—0
PAC Learning-Based Verification and Model Synthesis—0
PAC-Learning for Strategic Classification—0
PAC-learning gains of Turing machines over circuits and neural networks—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-learning in the presence of adversaries—0
PAC-learning in the presence of evasion adversaries—0
PAC Learning is just Bipartite Matching (Sort of)—0
PAC-learning is Undecidable—0
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