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PAC learning

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

Papers

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Showing 11–20 of 289 papers

TitleStatusHype
PAC Learning with Improvements—0
A Linear Theory of Multi-Winner Voting—0
Contrastive Learning with Nasty Noise—0
Towards Efficient Contrastive PAC Learning—0
Bandit Multiclass List Classification—0
On Agnostic PAC Learning in the Small Error Regime—0
Simplifying Adversarially Robust PAC Learning with Tolerance—0
On the Computability of Multiclass PAC Learning—0
Data-Driven Neural Certificate Synthesis—0
Efficient Optimal PAC Learning—0
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