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PAC learning

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

Papers

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Showing 21–30 of 289 papers

TitleStatusHype
A Linear Theory of Multi-Winner Voting—0
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise—0
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory—0
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise—0
An Optimal Elimination Algorithm for Learning a Best Arm—0
A packing lemma for VCN_k-dimension and learning high-dimensional data—0
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs—0
Adversarially Robust Learning with Tolerance—0
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