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PAC learning

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

Papers

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Showing 101–110 of 289 papers

TitleStatusHype
High-arity PAC learning via exchangeability—0
How to Use Heuristics for Differential Privacy—0
Identifying Information from Observations with Uncertainty and Novelty—0
Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models—0
Improved Algorithms for Collaborative PAC Learning—0
Incentive-aware PAC learning—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise—0
Distribution Learning Meets Graph Structure Sampling—0
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