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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
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers—0
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
Monotone Learning—0
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks—0
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine LearningCode0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Exponential Separation between Two Learning Models and Adversarial Robustness—0
On computable learning of continuous features—0
PAC-Learning Uniform Ergodic Communicative Networks—0
Realizable Learning is All You Need—0
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