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PAC learning

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

Papers

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Showing 231–240 of 289 papers

TitleStatusHype
Can SGD Learn Recurrent Neural Networks with Provable Generalization?—0
Learnability can be undecidable—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-learning in the presence of adversaries—0
How to Use Heuristics for Differential Privacy—0
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem—0
Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples—0
Locally Private Learning without Interaction Requires Separation—0
Learning Time Dependent Choice—0
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds—0
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