Time+Place: Tuesday 10/05/2016 14:30 Room 337-8 Taub Bld.
Title: Unsupervised Ensemble Learning
Speaker: Boaz Nadler - COLLOQUIUM LECTURE http://www.wisdom.weizmann.ac.il/~nadler/
Affiliation: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science
Host: Nir Ailon


In various applications, one is given the advice or predictions of several
classifiers of unknown reliability, over multiple questions or queries. 
This scenario is different from the standard supervised setting where 
classifier accuracy can be assessed from available labeled training or 
validation data, and raises several questions: given only the predictions 
of several classifiers of unknown accuracies, over a large set of unlabeled 
test data, is it possible to
a) reliably rank them, and
b) construct a meta-classifier more accurate than any individual classifier
in the ensemble?

In this talk we'll show that under various independence assumptions 
between classifier errors, this high dimensional data hides simple low 
dimensional structures. In particular, we'll present simple spectral methods 
to address the above questions, and derive new unsupervised spectral 
meta-learners.  We'll prove these methods are asymptotically consistent 
when the model assumptions hold, and also present their empirical success 
on a variety of unsupervised learning problems.

Short bio:

Boaz Nadler is an associate professor at the department of computer 
science and applied mathematics at the Weizmann Institute of Science. 
He holds a phd from Tel-Aviv university, spent 3 years at the applied 
math group at Yale university as a Gibbs assistant professor as well as 
a sabbatical year at Berkeley and Stanford.

His research interests are in statistical machine learning, mathematical 
statistics and applications in optics, signal and image processing.

Refreshments will be served from 14:15
Lecture starts at 14:30