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PAC learning

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

Papers

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Showing 31–40 of 289 papers

TitleStatusHype
Monotonic Learning in the PAC Framework: A New Perspective—0
Ensuring superior learning outcomes and data security for authorized learner—0
Of Dice and Games: A Theory of Generalized Boosting—0
Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models—0
Effective Littlestone Dimension—0
Probably Approximately Precision and Recall Learning—0
Learning multivariate Gaussians with imperfect advice—0
Reliable Learning of Halfspaces under Gaussian Marginals—0
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans—0
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