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PAC learning

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

Papers

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Showing 241–250 of 289 papers

TitleStatusHype
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Reliable Learning of Halfspaces under Gaussian Marginals—0
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees—0
Revisiting Agnostic PAC Learning—0
Robust learning under clean-label attack—0
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks—0
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity—0
Sample-Efficient Learning of Mixtures—0
Sample-efficient proper PAC learning with approximate differential privacy—0
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise—0
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