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PAC learning

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

Papers

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Showing 251–260 of 289 papers

TitleStatusHype
Superpolynomial Lower Bounds for Decision Tree Learning and Testing—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Symbolic Abstractions From Data: A PAC Learning Approach—0
The Optimal Sample Complexity of PAC Learning—0
The Power of Comparisons for Actively Learning Linear Classifiers—0
The Price is (Probably) Right: Learning Market Equilibria from Samples—0
The Sample Complexity of Multi-Distribution Learning for VC Classes—0
The sample complexity of multi-distribution learning—0
The VC-Dimension of Similarity Hypotheses Spaces—0
The working principles of model-based GAs fall within the PAC framework: A mathematical theory of problem decomposition—0
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