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PAC learning

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

Papers

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Showing 121–130 of 289 papers

TitleStatusHype
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean EstimationCode0
Learning versus Refutation in Noninteractive Local Differential Privacy—0
Is Out-of-Distribution Detection Learnable?—0
SQ Lower Bounds for Learning Single Neurons with Massart Noise—0
Superpolynomial Lower Bounds for Decision Tree Learning and Testing—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise—0
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data—0
Fine-Grained Distribution-Dependent Learning Curves—0
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
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