We describe a Bayesian method for group feature selection in linear regression problems. The method is based on a generalized version of the standard spike-and-slab prior distribution which is often used for individual feature selection. Exact Bayesian inference under the prior considered is infeasible for typical regression problems. However, approximate inference can be carried out efficiently using Expectation Propagation (EP). A detailed analysis of the generalized spike-and-slab prior shows that it is well suited for regression problems that are sparse at the group level. Furthermore, this prior can be used to introduce prior knowledge about specific groups of features that are a priori believed to be more relevant. An experimental eva...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
Variable selection in the linear regression model takes many apparent faces from both frequentist an...
Thesis (Ph.D.)--University of Washington, 2023Choosing a statistical model and accounting for uncert...
We describe a Bayesian method for group feature selection in linear regression problems. The method...
An expectation propagation (EP) algorithm is proposed for approximate inference in linear regression...
Group structures arise naturally in a variety of modern data applications and statistical problems i...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a...
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconcil...
We propose two multivariate extensions of the Bayesian group lasso for variable selection and estima...
In this work, we address the problem of solving a series of underdetermined linear inverse problembl...
We introduce Group Spike-and-slab Variational Bayes (GSVB), a scalable method for group sparse regre...
Challenging research in various fields has driven a wide range of methodological advances in variabl...
International audienceThe formulation of variable selection has been widely developed in the Bayesia...
In many real-world classification problems the input contains a large number of potentially ir-relev...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
Variable selection in the linear regression model takes many apparent faces from both frequentist an...
Thesis (Ph.D.)--University of Washington, 2023Choosing a statistical model and accounting for uncert...
We describe a Bayesian method for group feature selection in linear regression problems. The method...
An expectation propagation (EP) algorithm is proposed for approximate inference in linear regression...
Group structures arise naturally in a variety of modern data applications and statistical problems i...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a...
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconcil...
We propose two multivariate extensions of the Bayesian group lasso for variable selection and estima...
In this work, we address the problem of solving a series of underdetermined linear inverse problembl...
We introduce Group Spike-and-slab Variational Bayes (GSVB), a scalable method for group sparse regre...
Challenging research in various fields has driven a wide range of methodological advances in variabl...
International audienceThe formulation of variable selection has been widely developed in the Bayesia...
In many real-world classification problems the input contains a large number of potentially ir-relev...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
Variable selection in the linear regression model takes many apparent faces from both frequentist an...
Thesis (Ph.D.)--University of Washington, 2023Choosing a statistical model and accounting for uncert...