SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 31–40 of 289 papers

TitleStatusHype
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise—0
Agnostic Multi-Group Active Learning—0
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning—0
A Theory of PAC Learnability of Partial Concept Classes—0
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
A Unified Framework for Approximating and Clustering Data—0
Bagging is an Optimal PAC Learner—0
Bandit Multiclass List Classification—0
Active Learning for Contextual Search with Binary Feedbacks—0
Show:102550
← PrevPage 4 of 29Next →

No leaderboard results yet.