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PAC learning

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

Papers

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Showing 31–40 of 289 papers

TitleStatusHype
Adversarially Robust Learning with Tolerance—0
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks—0
A learning problem that is independent of the set theory ZFC axioms—0
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification—0
A Complete Characterization of Statistical Query Learning with Applications to Evolvability—0
Bagging is an Optimal PAC Learner—0
A Unified Framework for Approximating and Clustering Data—0
AI Reasoning Systems: PAC and Applied Methods—0
Bandit Multiclass List Classification—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
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