The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Stochastic methods for l 1 regularized loss minimization
Abstract
We describe and analyze two stochastic methods for l1 regularized loss minimization problems, such as the Lasso. The first method updates the weight of a single feature at each iteration while the second method updates the entire weight vector but only uses a single training example at each iteration. In both methods, the choice of feature/example is uniformly at random. Our theoretical runtime analysis suggests that the stochastic methods should outperform state-of-the-art deterministic approaches, including their deterministic counterparts, when the size of the problem is large. We demonstrate the advantage of stochastic methods by experimenting with synthetic and natural data sets.
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Cites
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Self-citationProblem complexity and method efficiency in optimization
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Cited by
Wright, S. J. (2015) · Mathematical Programming · doi:10.1007/s10107-015-0892-3 · Cited by 1,438
Optimization with Sparsity-Inducing Penalties
Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000015 · Cited by 730
Optimization with Sparsity-Inducing Penalties
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On the Convergence of Block Coordinate Descent Type Methods
Beck, A., & Tetruashvili, L. (2013) · SIAM Journal on Optimization · doi:10.1137/120887679 · Cited by 572
Optimal Distributed Online Prediction using Mini-Batches
Dekel, O., Gilad-Bachrach, R., Shamir, O., & Xiao, L. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1012.1367 · Cited by 512
Metadata
- Authors
- Shalev-Shwartz, S., & Tewari, A.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 929-936
- OpenAlex
- W1969048569
- Cited by
- 298
- References
- 36
- FWCI
- 10.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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