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PAC learning

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

Papers

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Showing 271–280 of 289 papers

TitleStatusHype
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
User-Level Differential Privacy With Few Examples Per User—0
-fractional Core Stability in Hedonic Games—0
VC Dimension and Distribution-Free Sample-Based Testing—0
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds—0
A Characterization of List Learnability—0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
A Characterization of Multiclass Learnability—0
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
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