Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

Semi-Supervised Learning

Paper titled The Information-Theoretic Value of Unlabeled Data in Semi-Supervised Learning by Alexander Golovnev, Dávid Pál and Balázs Szörényi accepted at ICML 2019.

The paper proves that unlabeled data beneficial for supervised learning tasks. We formalized the problem in the Probably Approximately Correct (PAC) model and we show that for learning projections over the Boolean hypercube {0,1}n one needs less labeled examples by a multiplicative factor Θ(log n) if one has access to unlabeled data.

About

No description, website, or topics provided.

Resources

Releases

No releases published

Packages

No packages published