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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
Computational-Statistical Tradeoffs from NP-hardness—0
A Distributional-Lifting Theorem for PAC Learning—0
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning—0
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
A packing lemma for VCN_k-dimension and learning high-dimensional data—0
From learnable objects to learnable random objects—0
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