Sequential Model-Based Ensemble Optimization

Alexandre Lacoste Laval University, Hugo Larochelle, Mario Marchand Laval University, François Laviolette Laval University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:706-714, 2014.

Abstract

One of the most tedious tasks in the applica- tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model- based optimization (SMBO) methods. This can be used to optimize a cross-validation perfor- mance of a learning algorithm over the value of its hyperparameters. However, it is well known that ensembles of learned models almost consis- tently outperform a single model, even if prop- erly selected. In this paper, we thus propose an extension of SMBO methods that automatically constructs such ensembles. This method builds on a recently proposed ensemble construction paradigm known as Agnostic Bayesian learning. In experiments on 22 regression and 39 classifi- cation data sets, we confirm the success of this proposed approach, which is able to outperform model selection with SMBO.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14t, title = {Sequential Model-Based Ensemble Optimization}, author = {University, Alexandre Lacoste Laval and Larochelle, Hugo and University, Mario Marchand Laval and University, Fran{\c{c}}ois Laviolette Laval}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {706--714}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14t/university14t.pdf}, url = {https://proceedings.mlr.press/r12/university14t.html}, abstract = {One of the most tedious tasks in the applica- tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model- based optimization (SMBO) methods. This can be used to optimize a cross-validation perfor- mance of a learning algorithm over the value of its hyperparameters. However, it is well known that ensembles of learned models almost consis- tently outperform a single model, even if prop- erly selected. In this paper, we thus propose an extension of SMBO methods that automatically constructs such ensembles. This method builds on a recently proposed ensemble construction paradigm known as Agnostic Bayesian learning. In experiments on 22 regression and 39 classifi- cation data sets, we confirm the success of this proposed approach, which is able to outperform model selection with SMBO.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Sequential Model-Based Ensemble Optimization %A Alexandre Lacoste Laval University %A Hugo Larochelle %A Mario Marchand Laval University %A François Laviolette Laval University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14t %I PMLR %P 706--714 %U https://proceedings.mlr.press/r12/university14t.html %V R12 %X One of the most tedious tasks in the applica- tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model- based optimization (SMBO) methods. This can be used to optimize a cross-validation perfor- mance of a learning algorithm over the value of its hyperparameters. However, it is well known that ensembles of learned models almost consis- tently outperform a single model, even if prop- erly selected. In this paper, we thus propose an extension of SMBO methods that automatically constructs such ensembles. This method builds on a recently proposed ensemble construction paradigm known as Agnostic Bayesian learning. In experiments on 22 regression and 39 classifi- cation data sets, we confirm the success of this proposed approach, which is able to outperform model selection with SMBO. %Z Reissued by PMLR on 04 October 2026.
APA
University, A.L.L., Larochelle, H., University, M.M.L. & University, F.L.L.. (2014). Sequential Model-Based Ensemble Optimization. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:706-714 Available from https://proceedings.mlr.press/r12/university14t.html. Reissued by PMLR on 04 October 2026.

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