Title: Computationally and statistically efficient methods for model selection in graphical models
Speaker: Kevin Murphy (Stats/CS, U. British Columbia)
Time/Place: Friday, 11am in Macdonald-Harrington G-01
Abstract:
Graphical models are a way of representing conditional independence relationships between random variables using graphs. In this talk, we discuss ways of learning the structure of graphs from data. This is useful for visualizing relationships between variables in high-dimensional data, as well as for building density models for use in prediction, classification, clustering, etc. We will focus on undirected graphs (also called random fields), and in particular on methods based on L1-penalized maximum likelihood. First we extend existing results for the Gaussian and binary case to the more general case of conditional random fields and multi-state models. This requires that we replace the L1 penalty with a group L1 penalty, which poses various computational challenges. The second extension is to estimate the group structure (by clustering the variables) while simultaneously learning the graph structure. This technique relies on new bounds on the partition function for the positive definite matrix Laplace distribution, which also has applications in hierarchical Bayesian analysis of multiple related graphs.
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To make things clearer:
The talk is this coming Friday (March 27th) at 11am. The location is Macdonald-Harrington G-01, which is at McGill university.
Sorry for the confusion, Dumitru
On Tue, Mar 24, 2009 at 16:33, Dumitru Erhan dumitru.erhan@umontreal.ca wrote:
Title: Computationally and statistically efficient methods for model selection in graphical models
Speaker: Kevin Murphy (Stats/CS, U. British Columbia)
Time/Place: Friday, 11am in Macdonald-Harrington G-01
Abstract:
Graphical models are a way of representing conditional independence relationships between random variables using graphs. In this talk, we discuss ways of learning the structure of graphs from data. This is useful for visualizing relationships between variables in high-dimensional data, as well as for building density models for use in prediction, classification, clustering, etc. We will focus on undirected graphs (also called random fields), and in particular on methods based on L1-penalized maximum likelihood. First we extend existing results for the Gaussian and binary case to the more general case of conditional random fields and multi-state models. This requires that we replace the L1 penalty with a group L1 penalty, which poses various computational challenges. The second extension is to estimate the group structure (by clustering the variables) while simultaneously learning the graph structure. This technique relies on new bounds on the partition function for the positive definite matrix Laplace distribution, which also has applications in hierarchical Bayesian analysis of multiple related graphs.
Reminder:
---------- Forwarded message ---------- From: Dumitru Erhan dumitru.erhan@umontreal.ca Date: Wed, Mar 25, 2009 at 12:54 Subject: Re: Kevin Murphy @ McGill on Friday To: lisa_seminaires@iro.umontreal.ca
To make things clearer:
The talk is this coming Friday (March 27th) at 11am. The location is Macdonald-Harrington G-01, which is at McGill university.
Sorry for the confusion, Dumitru
On Tue, Mar 24, 2009 at 16:33, Dumitru Erhan dumitru.erhan@umontreal.ca wrote:
Title: Computationally and statistically efficient methods for model selection in graphical models
Speaker: Kevin Murphy (Stats/CS, U. British Columbia)
Time/Place: Friday, 11am in Macdonald-Harrington G-01
Abstract:
Graphical models are a way of representing conditional independence relationships between random variables using graphs. In this talk, we discuss ways of learning the structure of graphs from data. This is useful for visualizing relationships between variables in high-dimensional data, as well as for building density models for use in prediction, classification, clustering, etc. We will focus on undirected graphs (also called random fields), and in particular on methods based on L1-penalized maximum likelihood. First we extend existing results for the Gaussian and binary case to the more general case of conditional random fields and multi-state models. This requires that we replace the L1 penalty with a group L1 penalty, which poses various computational challenges. The second extension is to estimate the group structure (by clustering the variables) while simultaneously learning the graph structure. This technique relies on new bounds on the partition function for the positive definite matrix Laplace distribution, which also has applications in hierarchical Bayesian analysis of multiple related graphs.
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Dumitru Erhan