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PAC learning

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

Papers

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Showing 11–20 of 289 papers

TitleStatusHype
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration—0
PAC Learning with Improvements—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
A Linear Theory of Multi-Winner Voting—0
Contrastive Learning with Nasty Noise—0
Towards Efficient Contrastive PAC Learning—0
Bandit Multiclass List Classification—0
On Agnostic PAC Learning in the Small Error Regime—0
Simplifying Adversarially Robust PAC Learning with Tolerance—0
On the Computability of Multiclass PAC Learning—0
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