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PAC learning

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

Papers

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Showing 161–170 of 289 papers

TitleStatusHype
Forster Decomposition and Learning Halfspaces with Noise—0
Semi-verified PAC Learning from the Crowd—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Multi-group Agnostic PAC Learnability—0
Incentive-aware PAC learning—0
Learning and Certification under Instance-targeted Poisoning—0
Broadly Applicable Targeted Data Sample Omission Attacks—0
Symbolic Abstractions From Data: A PAC Learning Approach—0
PAC-learning gains of Turing machines over circuits and neural networks—0
Robust learning under clean-label attack—0
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