Ensemble methods for convex regression with applications to geometric programming based circuit design
Publication
, Journal Article
Hannah, LA; Dunson, DB
Published in: Proceedings of the 29th International Conference on Machine Learning, ICML 2012
October 10, 2012
Convex regression is a promising area for bridging statistical estimation and deterministic convex optimization. New piecewise linear convex regression methods (Hannah and Dunson, 2011; Magnani and Boyd, 2009) are fast and scalable, but can have instability when used to approximate constraints or objective functions for optimization. Ensemble methods, like bagging, smearing and random partitioning, can alleviate this problem and maintain the theoretical properties of the underlying estimator. We empirically examine the performance of ensemble methods for prediction and optimization, and then apply them to device modeling and constraint approximation for geometric programming based circuit design. Copyright 2012 by the author(s)/owner(s).
Duke Scholars
Published In
Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Publication Date
October 10, 2012
Volume
1
Start / End Page
369 / 376
Citation
APA
Chicago
ICMJE
MLA
NLM
Hannah, L. A., & Dunson, D. B. (2012). Ensemble methods for convex regression with applications to geometric programming based circuit design. Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 1, 369–376.
Hannah, L. A., and D. B. Dunson. “Ensemble methods for convex regression with applications to geometric programming based circuit design.” Proceedings of the 29th International Conference on Machine Learning, ICML 2012 1 (October 10, 2012): 369–76.
Hannah LA, Dunson DB. Ensemble methods for convex regression with applications to geometric programming based circuit design. Proceedings of the 29th International Conference on Machine Learning, ICML 2012. 2012 Oct 10;1:369–76.
Hannah, L. A., and D. B. Dunson. “Ensemble methods for convex regression with applications to geometric programming based circuit design.” Proceedings of the 29th International Conference on Machine Learning, ICML 2012, vol. 1, Oct. 2012, pp. 369–76.
Hannah LA, Dunson DB. Ensemble methods for convex regression with applications to geometric programming based circuit design. Proceedings of the 29th International Conference on Machine Learning, ICML 2012. 2012 Oct 10;1:369–376.
Published In
Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Publication Date
October 10, 2012
Volume
1
Start / End Page
369 / 376