A scalable modular convex solver for regularized risk minimization

Teo, CH; Smola, A; Vishwanathan, SVN; Le, QV

doi:10.1145/1281192.1281270

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A scalable modular convex solver for regularized risk minimization

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Le, QV
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?doid=1281192.1281270
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0003-D805-4

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.