[Statlist] Friday, January 15, 2016 with Benjamin Frot (Oxford University Statistics)

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf
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We are glad to announce the following talk:

Friday, January 15, 2016 at 15.15h  ETH Zurich HG G 19.141
with Benjamin Frot (Oxford University Statistics)
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Title:

Latent variable model selection for Gaussian conditional random fields

Abstract:

We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as "sparsistent" (i.e. capable of recovering the graph structure). We then describe a proximal gradient algorithm which is able to fit the model to thousands of variables. Through extensive simulations, we illustrate the conditions required for identifiability and show that there is a wide range of situations in which this model performs significantly better than its counterparts. We also show how this problem is relevant to some of the challenges faced by instrumental variable methods.


This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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