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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Open accessAbstract
We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results for various key machine learning optimization problems including SVM, logistic regression, ridge regression, Lasso, and multiclass SVM. Experiments validate our theoretical findings.
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Cites
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Self-citationEfficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
Nesterov, Y. (2012) · SIAM Journal on Optimization · doi:10.1137/100802001 · Cited by 1,187
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Cotter, A., Shamir, O., Srebro, N., & Sridharan, K. (2011) · arXiv · doi:10.48550/arxiv.1106.4574 · Cited by 173
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SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Defazio, A., Bach, F., & Lacoste-Julien, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.0202 · Cited by 834
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749
Wang, F., Xuan, Z., Zhen, Z., Li, K., Wang, T., & Shi, M. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.112766 · Cited by 639
Machine translation of cortical activity to text with an encoder–decoder framework
Makin, J. G., Moses, D. A., & Chang, E. F. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0608-8 · Cited by 321
Metadata
- Authors
- Shalev-Shwartz, S., & Zhang, T.
- Venue
- Mathematical Programming
- Published
- 2014-11-20
- Volume
- 155
- Issue
- 1-2
- Pages
- 105-145
- ISSN
- 0025-5610 (ISSN-L)
- 1436-4646
- OpenAlex
- W2118545728
- Cited by
- 360
- References
- 4
- FWCI
- 49.85
- Type
- journal article
- Language
- en
- Open access
- green
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