ML Anthology

The Bayesian Group-Lasso for Analyzing Contingency Tables

Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth
ICML 2009 pp. 881-888
doi:10.1145/1553374.1553487 /icml/2009/raman2009icml-bayesian/

Abstract

Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.

PDF ICML Semantic Scholar

Cite

Text

Raman et al. "The Bayesian Group-Lasso for Analyzing Contingency Tables." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553487

Markdown

[Raman et al. "The Bayesian Group-Lasso for Analyzing Contingency Tables." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/raman2009icml-bayesian/) doi:10.1145/1553374.1553487

BibTeX

@inproceedings{raman2009icml-bayesian,
  title     = {{The Bayesian Group-Lasso for Analyzing Contingency Tables}},
  author    = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Roth, Volker},
  booktitle = {International Conference on Machine Learning},
  year      = {2009},
  pages     = {881-888},
  doi       = {10.1145/1553374.1553487},
  url       = {https://mlanthology.org/icml/2009/raman2009icml-bayesian/}
}