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PAC learning

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

Papers

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Showing 71–80 of 289 papers

TitleStatusHype
List Sample Compression and Uniform Convergence—0
Towards a theory of model distillationCode0
Majority-of-Three: The Simplest Optimal Learner?—0
Proper vs Improper Quantum PAC learning—0
High-arity PAC learning via exchangeability—0
Private PAC Learning May be Harder than Online Learning—0
Collaborative Learning with Different Labeling Functions—0
Transductive Learning Is Compact—0
The sample complexity of multi-distribution learning—0
-fractional Core Stability in Hedonic Games—0
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