SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 161–170 of 289 papers

TitleStatusHype
On Agnostic PAC Learning in the Small Error Regime—0
On Agnostic PAC Learning using L_2-polynomial Regression and Fourier-based Algorithms—0
On computable learning of continuous features—0
On Fundamental Limits of Robust Learning—0
On Generalization Bounds of a Family of Recurrent Neural Networks—0
On Learnability with Computable Learners—0
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers—0
Online Learning and Disambiguations of Partial Concept Classes—0
Online Learning of k-CNF Boolean Functions—0
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data—0
Show:102550
← PrevPage 17 of 29Next →

No leaderboard results yet.