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PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering

2009

Conference Paper

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We derive a PAC-Bayesian generalization bound for density estimation. Similar to the PAC-Bayesian generalization bound for classification, the result has the appealingly simple form of a tradeoff between empirical performance and the KL-divergence of the posterior from the prior. Moreover, the PAC-Bayesian generalization bound for classification can be derived as a special case of the bound for density estimation. To illustrate a possible application of our bound we derive a generalization bound for co-clustering. The bound provides a criterion to evaluate the ability of co-clustering to predict new co-occurrences, thus introducing a supervised flavor to this traditionally unsupervised task.

Author(s): Seldin, Y. and Tishby, N.
Book Title: JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009
Journal: In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009)
Pages: 472-479
Year: 2009
Month: April
Day: 0
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 12th International Conference on Artificial Intelligence and Statistics
Event Place: Clearwater Beach, FL, USA

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{6592,
  title = {PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering},
  author = {Seldin, Y. and Tishby, N.},
  journal = {In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009},
  pages = {472-479},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = apr,
  year = {2009},
  month_numeric = {4}
}