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PAC learning

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

Papers

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Showing 1–10 of 289 papers

TitleStatusHype
Lean Formalization of Generalization Error Bound by Rademacher ComplexityCode1
Prospective Learning: Learning for a Dynamic FutureCode1
VICE: Variational Interpretable Concept EmbeddingsCode1
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
A Computational Separation between Private Learning and Online Learning—0
Active-learning-based non-intrusive Model Order Reduction—0
A Complete Characterization of Statistical Query Learning with Applications to Evolvability—0
A Distributional-Lifting Theorem for PAC Learning—0
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification—0
Active Learning for Contextual Search with Binary Feedbacks—0
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