Sparse Bayesian Nonparametric Regression
Abstract
One of the most common problems in machine learning and statistics consists of estimating the mean response X.beta from a vector of observations y assuming y=X.beta+epsilon where X is known, beta is a vector of parameters of interest and epsilon a vector of stochastic errors. We are particularly interested here in the case where the dimension K of beta is much higher than the dimension of y. We propose some flexible Bayesian models which can yield sparse estimates of beta. We show that as K tends to infinity, these models are closely related to a class of Levy processes. Simulations demonstrate that our models outperform significantly a range of popular alternatives.
Cite
Text
Caron and Doucet. "Sparse Bayesian Nonparametric Regression." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390168Markdown
[Caron and Doucet. "Sparse Bayesian Nonparametric Regression." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/caron2008icml-sparse/) doi:10.1145/1390156.1390168BibTeX
@inproceedings{caron2008icml-sparse,
title = {{Sparse Bayesian Nonparametric Regression}},
author = {Caron, Francois and Doucet, Arnaud},
booktitle = {International Conference on Machine Learning},
year = {2008},
pages = {88-95},
doi = {10.1145/1390156.1390168},
url = {https://mlanthology.org/icml/2008/caron2008icml-sparse/}
}