Bayes Seminar: David Wolpert [CCM]

3rd Floor Classroom/3-Flatiron Institute (162 5th Avenue)

3rd Floor Classroom/3-Flatiron Institute

162 5th Avenue


Discussion Lead: David Wolpert [CCM]

Topic: Want to use cross-validation? Use stacking instead

Abstract:  Cross-validation is perhaps the most commonly used technique in

machine learning and statistics. Indeed, it can be viewed as a formalization of
the scientific method. Cross-validation is, at its core, a winner-take-all
meta-supervised learning algorithm, run over a meta-data set whose input space is the set of 
predictions by all candidate algorithms on held-out points, and whose output is the
associated truths in those held-out points. Stacking is the simple idea to replace
cross-validation’s winner-take-all algorithm with a more sophisticate learning algorithm. 
In this talk I review some of the experimental demonstrations of stacking’s power, 
in domains ranging from supervised learning to unsupervised learning to Monte Carlo
integral estimation to community detection in networks.  

The agenda of this meeting is empty