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PAC learning

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

Papers

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Showing 61–70 of 289 papers

TitleStatusHype
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
On the Computational Landscape of Replicable Learning—0
Distribution Learning Meets Graph Structure Sampling—0
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks—0
Is Transductive Learning Equivalent to PAC Learning?—0
Error Exponent in Agnostic PAC Learning—0
On the Power of Interactive Proofs for Learning—0
On the Learnability of Out-of-distribution Detection—0
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs—0
Hardness of Learning Boolean Functions from Label Proportions—0
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