UTCS Artificial Intelligence
courses
talks/events
demos
people
projects
publications
software/data
labs
areas
admin
On the Use of Variational Inference for Learning Discrete Graphical Models (2011)
Eunho Yang
and
Pradeep Ravikumar
We study the general class of estimators for graphical model structure based on optimizing ell_1-regularized approximate log-likelihood, where the approximate likelihood uses tractable variational approximations of the partition function. We provide a message-passing algorithm that directly computes the ell_1 regularized approximate MLE. Further, in the case of certain reweighted entropy approximations to the partition function, we show that surprisingly the ell_1 regularized approximate MLE estimator has a closed-form, so that we would no longer need to run through many iterations of approximate inference and message-passing. Lastly, we analyze this general class of estimators for graph structure recovery, or its sparsistency, and show that it is indeed sparsistent under certain conditions.
View:
PDF
Citation:
In
International Conference on Machine learning (ICML)
2011.
Bibtex:
@inproceedings{YR11, title={On the Use of Variational Inference for Learning Discrete Graphical Models}, author={Eunho Yang and Pradeep Ravikumar}, booktitle={International Conference on Machine learning (ICML)}, series={28}, url="http://www.cs.utexas.edu/users/ai-lab?YR11", year={2011} }
People
Pradeep Ravikumar
Formerly affiliated Faculty
pradeepr [at] cs utexas edu
Eunho Yang
Ph.D. Alumni
eunho [at] cs utexas edu
Areas of Interest
Graphical Models
Machine Learning
Statistical Learning