@poster{6329,
  title = {PAC-Bayesian Bounds for Discrete Density Estimation and Co-clustering Analysis},
  journal = {Workshop "Foundations and New Trends of PAC Bayesian Learning"},
  abstract = {We applied PAC-Bayesian framework to derive gen-
  eralization bounds for co-clustering1. The analysis
  yielded regularization terms that were absent in the
  preceding formulations of this task. The bounds sug-
  gested that co-clustering should optimize a trade-off
  between its empirical performance and the mutual in-
  formation that the cluster variables preserve on row
  and column indices. Proper regularization enabled
  us to achieve state-of-the-art results in prediction of
  the missing ratings in the MovieLens collaborative
  filtering dataset.
  In addition a PAC-Bayesian bound for discrete den-
  sity estimation was derived. We have shown that
  the PAC-Bayesian bound for classification is a spe-
  cial case of the PAC-Bayesian bound for discrete den-
  sity estimation. We further introduced combinatorial
  priors to PAC-Bayesian analysis. The combinatorial
  priors are more appropriate for discrete domains, as
  opposed to Gaussian priors, the latter of which are
  suitable for continuous domains. It was shown that
  combinatorial priors lead to regularization terms in
  the form of mutual information.},
  volume = {2010},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2010},
  slug = {6329},
  author = {Seldin, Y. and Tishby, N.},
  month_numeric = {3}
}