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PAC learning

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

Papers

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Showing 211–220 of 289 papers

TitleStatusHype
Towards a theory of out-of-distribution learning—0
Towards Efficient Contrastive PAC Learning—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Tree Learning: Optimal Algorithms and Sample Complexity—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
User-Level Differential Privacy With Few Examples Per User—0
-fractional Core Stability in Hedonic Games—0
VC Dimension and Distribution-Free Sample-Based Testing—0
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds—0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
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