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PAC learning

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

Papers

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Showing 181–190 of 289 papers

TitleStatusHype
On the Role of Entanglement and Statistics in Learning—0
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences—0
On the Sample Complexity of Adversarial Multi-Source PAC Learning—0
On the Sample Complexity of Learning Sum-Product Networks—0
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem—0
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas—0
Optimal Learners for Realizable Regression: PAC Learning and Online Learning—0
Optimal lower bounds for Quantum Learning via Information Theory—0
Order-Revealing Encryption and the Hardness of Private Learning—0
Overview of AdaBoost : Reconciling its views to better understand its dynamics—0
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