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  A scalable modular convex solver for regularized risk minimization

Teo, C., Smola, A., Vishwanathan, S., & Le, Q. (2007). A scalable modular convex solver for regularized risk minimization. In P. Berkhin, R. Caruana, & X. Wu (Eds.), KDD '07: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 727-736). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0003-D805-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-D806-3
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1281192.1281270 (Publisher version)
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 Creators:
Teo, CH, Author
Smola, A, Author           
Vishwanathan, SVN, Author
Le, QV1, 2, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as l1 and l2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox.

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 Dates: 2007-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1281192.1281270
 Degree: -

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Title: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07)
Place of Event: San Jose, CA, USA
Start-/End Date: 2007-08-12 - 2007-08-15

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Title: KDD '07: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Source Genre: Proceedings
 Creator(s):
Berkhin, P, Editor
Caruana, R, Editor
Wu, X, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 727 - 736 Identifier: ISBN: 978-1-59593-609-7