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PAC learning

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

Papers

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Showing 131–140 of 289 papers

TitleStatusHype
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
Generalization Bounds for Data-Driven Numerical Linear Algebra—0
PAC Generalization via Invariant Representations—0
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization—0
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks—0
Clifford Circuits can be Properly PAC Learned if and only if RP=NP—0
Active-learning-based non-intrusive Model Order Reduction—0
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability—0
A Characterization of Multiclass Learnability—0
Adversarially Robust Learning with Tolerance—0
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