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PAC learning

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

Papers

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Showing 141–150 of 289 papers

TitleStatusHype
Lifting uniform learners via distributional decomposition—0
List Learning with Attribute Noise—0
List Sample Compression and Uniform Convergence—0
From learnable objects to learnable random objects—0
Lower Bounds for Adversarially Robust PAC Learning—0
Low-Rank MDPs with Continuous Action Spaces—0
Majority-of-Three: The Simplest Optimal Learner?—0
Markov Decision Processes with Continuous Side Information—0
Measurability in the Fundamental Theorem of Statistical Learning—0
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability—0
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