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PAC learning

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

Papers

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Showing 81–90 of 289 papers

TitleStatusHype
Information-theoretic generalization bounds for learning from quantum data—0
Low-Rank MDPs with Continuous Action Spaces—0
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs—0
PAC Learning Linear Thresholds from Label Proportions—0
Overview of AdaBoost : Reconciling its views to better understand its dynamics—0
Distributional PAC-Learning from Nisan's Natural Proofs—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
User-Level Differential Privacy With Few Examples Per User—0
Provable learning of quantum states with graphical models—0
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