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PAC learning

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

Papers

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Showing 41–50 of 289 papers

TitleStatusHype
Measurability in the Fundamental Theorem of Statistical Learning—0
Learning Linear Attention in Polynomial Time—0
Strategic Classification With Externalities—0
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data—0
Agnostic Smoothed Online Learning—0
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate—0
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians—0
Derandomizing Multi-Distribution Learning—0
Fast decision tree learning solves hard coding-theoretic problems—0
A Practical Theory of Generalization in Selectivity Learning—0
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