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PAC learning

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

Papers

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Showing 261–270 of 289 papers

TitleStatusHype
Strategic Classification With Externalities—0
Superconstant Inapproximability of Decision Tree Learning—0
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs—0
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
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