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PAC learning

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

Papers

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Showing 51–60 of 289 papers

TitleStatusHype
Computational-Statistical Tradeoffs from NP-hardness—0
Computing the Vapnik Chervonenkis Dimension for Non-Discrete Settings—0
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning—0
Contrastive Learning with Nasty Noise—0
Credit Attribution and Stable Compression—0
Crowdsourced PAC Learning under Classification Noise—0
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
Data-Driven Neural Certificate Synthesis—0
Decidability of Sample Complexity of PAC Learning in finite setting—0
Active Learning for Contextual Search with Binary Feedbacks—0
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