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PAC learning

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

Papers

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Showing 101–125 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
Information-theoretic generalization bounds for learning from quantum data—0
Credit Attribution and Stable Compression—0
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?—0
Is Out-of-Distribution Detection Learnable?—0
Is Transductive Learning Equivalent to PAC Learning?—0
Active Learning for Contextual Search with Binary Feedbacks—0
Learnability can be undecidable—0
Transductive Learning Is Compact—0
Learnability with PAC Semantics for Multi-agent Beliefs—0
Learnable: Theory vs Applications—0
Learning and Certification under Instance-targeted Poisoning—0
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—0
Learning Concepts Definable in First-Order Logic with Counting—0
Learning DNF Expressions from Fourier Spectrum—0
Learning from Mixtures of Private and Public Populations—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
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