An Upper Bound on the Global Optimum in Parameter Estimation

Khaled Refaat UCLA, Adnan Darwiche UCLA
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:348-357, 2015.

Abstract

Learning graphical model parameters from incomplete data is a non-convex optimization problem. Iterative algorithms, such as Expectation Maximization (EM), can be used to get a local optimum solution. However, little is known about the quality of the learned local optimum, compared to the unknown global optimum. We exploit variables that are always observed in the dataset to get an upper bound on the global optimum which can give insight into the quality of the parameters learned by estimation algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-ucla15a, title = {An Upper Bound on the Global Optimum in Parameter Estimation}, author = {UCLA, Khaled Refaat and UCLA, Adnan Darwiche}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {348--357}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/ucla15a/ucla15a.pdf}, url = {https://proceedings.mlr.press/r13/ucla15a.html}, abstract = {Learning graphical model parameters from incomplete data is a non-convex optimization problem. Iterative algorithms, such as Expectation Maximization (EM), can be used to get a local optimum solution. However, little is known about the quality of the learned local optimum, compared to the unknown global optimum. We exploit variables that are always observed in the dataset to get an upper bound on the global optimum which can give insight into the quality of the parameters learned by estimation algorithms.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T An Upper Bound on the Global Optimum in Parameter Estimation %A Khaled Refaat UCLA %A Adnan Darwiche UCLA %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-ucla15a %I PMLR %P 348--357 %U https://proceedings.mlr.press/r13/ucla15a.html %V R13 %X Learning graphical model parameters from incomplete data is a non-convex optimization problem. Iterative algorithms, such as Expectation Maximization (EM), can be used to get a local optimum solution. However, little is known about the quality of the learned local optimum, compared to the unknown global optimum. We exploit variables that are always observed in the dataset to get an upper bound on the global optimum which can give insight into the quality of the parameters learned by estimation algorithms. %Z Reissued by PMLR on 04 October 2026.
APA
UCLA, K.R. & UCLA, A.D.. (2015). An Upper Bound on the Global Optimum in Parameter Estimation. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:348-357 Available from https://proceedings.mlr.press/r13/ucla15a.html. Reissued by PMLR on 04 October 2026.

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