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PAC learning

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

Papers

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Showing 51–75 of 289 papers

TitleStatusHype
Computational-Statistical Tradeoffs from NP-hardness—0
Computing the Vapnik Chervonenkis Dimension for Non-Discrete Settings—0
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning—0
Contrastive Learning with Nasty Noise—0
Credit Attribution and Stable Compression—0
Crowdsourced PAC Learning under Classification Noise—0
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
Data-Driven Neural Certificate Synthesis—0
Decidability of Sample Complexity of PAC Learning in finite setting—0
Derandomizing Multi-Distribution Learning—0
Differentially Private Learning of Geometric Concepts—0
Differentially Private Release and Learning of Threshold Functions—0
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise—0
Agnostic Multi-Group Active Learning—0
Distribution-Independent Reliable Learning—0
Distribution Learnability and Robustness—0
Distribution Learning Meets Graph Structure Sampling—0
Distribution-Specific Agnostic Conditional Classification With Halfspaces—0
Do PAC-Learners Learn the Marginal Distribution?—0
Effective Littlestone Dimension—0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials—0
Efficient Optimal PAC Learning—0
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks—0
Active Learning for Contextual Search with Binary Feedbacks—0
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