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PAC learning

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

Papers

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Showing 41–50 of 289 papers

TitleStatusHype
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization—0
Broadly Applicable Targeted Data Sample Omission Attacks—0
Can SGD Learn Recurrent Neural Networks with Provable Generalization?—0
Characterizing the Sample Complexity of Private Learners—0
Clifford Circuits can be Properly PAC Learned if and only if RP=NP—0
Closure Properties for Private Classification and Online Prediction—0
Collaborative Learning with Different Labeling Functions—0
Collaborative PAC Learning—0
Communication-Aware Collaborative Learning—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
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