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PAC learning

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

Papers

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Showing 181–190 of 289 papers

TitleStatusHype
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise—0
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models—0
Sample-efficient proper PAC learning with approximate differential privacy—0
VC Dimension and Distribution-Free Sample-Based Testing—0
PAC-Learning for Strategic Classification—0
Towards a Combinatorial Characterization of Bounded-Memory Learning—0
Efficient PAC Learning from the Crowd with Pairwise Comparisons—0
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—0
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryCode0
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