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PAC learning

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

Papers

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Showing 71–80 of 289 papers

TitleStatusHype
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
Efficient PAC Learning from the Crowd—0
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate—0
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians—0
Ehrenfeucht-Haussler Rank and Chain of Thought—0
Enhancing PAC Learning of Half spaces Through Robust Optimization Techniques—0
Active Learning for Contextual Search with Binary Feedbacks—0
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