@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Martha White's publication directory www.cs.ualberta.ca/~whitem/research
@InProceedings(12aistats-reverse,
  Title = "Generalized Optimal Reverse Prediction",
  Author = "Martha White and Dale Schuurmans",
  Abstract="Recently it has been shown that classical supervised and unsupervised
training methods can be unified as special cases of so-called
``optimal reverse prediction'': 
predicting inputs from target labels
while optimizing over both model parameters and missing labels.
Although this perspective establishes links between
classical training principles, 
the existing formulation only applies to linear predictors 
under squared loss, hence is extremely limited.
We generalize the formulation of optimal reverse prediction 
to arbitrary Bregman divergences, and more importantly to non-linear 
predictors. 
This extension is achieved
by establishing a new, generalized form of forward-reverse
minimization equivalence that holds for 
arbitrary matching losses.
Several benefits follow.
First, a new variant of Bregman divergence clustering can be recovered
that incorporates a non-linear data reconstruction model.
Second, normalized-cut and kernel-based extensions 
can be formulated coherently.
Finally, a new semi-supervised training principle can be recovered
for classification problems that 
demonstrates some advantages over the state of the art.",
  Year = "2012",
  Booktitle = "Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS)",
  Pages = "1305--1313",
  AcceptRate = "< 30\%",
  AcceptNumbers = "TBA"
  bib2html_dl_pdf="../publications/12aistats-reverse.pdf"
 )