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PAC learning

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

Papers

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Showing 151–160 of 289 papers

TitleStatusHype
Monotone Learning—0
Monotonic Learning in the PAC Framework: A New Perspective—0
More data speeds up training time in learning halfspaces over sparse vectors—0
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria—0
Multiclass versus Binary Differentially Private PAC Learning—0
Multi-group Agnostic PAC Learnability—0
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees—0
Multi-step learning and underlying structure in statistical models—0
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration—0
Of Dice and Games: A Theory of Generalized Boosting—0
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