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PAC learning

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

Papers

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Showing 171–180 of 289 papers

TitleStatusHype
On the Complexity of Learning from Label Proportions—0
On the complexity of PAC learning in Hilbert spaces—0
On the Computability of Multiclass PAC Learning—0
On the Computability of Robust PAC Learning—0
On the Computational Landscape of Replicable Learning—0
On the Hardness of PAC-learning Stabilizer States with Noise—0
On the Learnability of Out-of-distribution Detection—0
On the Power of Differentiable Learning versus PAC and SQ Learning—0
On the Power of Interactive Proofs for Learning—0
On the Power of Learning from k-Wise Queries—0
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