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PAC learning

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

Papers

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Showing 201–210 of 289 papers

TitleStatusHype
Agnostic Learning of a Single Neuron with Gradient Descent—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
On the Complexity of Learning from Label Proportions—0
Closure Properties for Private Classification and Online Prediction—0
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Decidability of Sample Complexity of PAC Learning in finite setting—0
On the Sample Complexity of Adversarial Multi-Source PAC Learning—0
Best-item Learning in Random Utility Models with Subset Choices—0
Quantum statistical query learning—0
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