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PAC learning

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

Papers

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Showing 241–250 of 289 papers

TitleStatusHype
PAC-learning is Undecidable—0
Learnable: Theory vs Applications—0
AI Reasoning Systems: PAC and Applied Methods—0
PAC-learning in the presence of evasion adversaries—0
Private PAC learning implies finite Littlestone dimension—0
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights—0
Improved Algorithms for Collaborative PAC Learning—0
Privacy-preserving Prediction—0
Tight Lower Bounds for Locally Differentially Private Selection—0
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees—0
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