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PAC learning

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

Papers

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Showing 251–260 of 289 papers

TitleStatusHype
Collaborative PAC Learning—0
Markov Decision Processes with Continuous Side Information—0
A learning problem that is independent of the set theory ZFC axioms—0
Learning under p-Tampering Attacks—0
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory—0
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time—0
Agnostic Learning by Refuting—0
Learning Geometric Concepts with Nasty Noise—0
Sample-Efficient Learning of Mixtures—0
On Fundamental Limits of Robust Learning—0
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