Learnability and stability in the general learning setting

Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations


We establish that stability is necessary and sufficient for learning, even in the General Learning Setting where uniform convergence conditions are not necessary for learning, and where learning might only be possible with a non-ERM learning rule. This goes beyond previous work on the relationship between stability and learnability, which focused on supervised classification and regression, where learnability is equivalent to uniform convergence and it is enough to consider the ERM.

Original languageAmerican English
StatePublished - 2009
Externally publishedYes
Event22nd Conference on Learning Theory, COLT 2009 - Montreal, QC, Canada
Duration: 18 Jun 200921 Jun 2009


Conference22nd Conference on Learning Theory, COLT 2009
CityMontreal, QC


Dive into the research topics of 'Learnability and stability in the general learning setting'. Together they form a unique fingerprint.

Cite this