The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A scalable modular convex solver for regularized risk minimization
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Self-citationTools for privacy preserving distributed data mining
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772867 · Cited by 890
Cited by
Dataset Shift in Machine Learning
(2008) · doi:10.7551/mitpress/9780262170055.001.0001 · Cited by 1,553
Scaling Distributed Machine Learning with the Parameter Server
Li, M. (2014) · Proceedings of the 2014 International Conference on Big Data Science and Computing · doi:10.1145/2640087.2644155 · Cited by 979
Cutting-plane training of structural SVMs
Joachims, T., Finley, T., & Yu, C.-N. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5108-8 · Cited by 958
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
Roux, N. L., Schmidt, M., & Bach, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.6258 · Cited by 481
Learning to rank in person re-identification with metric ensembles
Paisitkriangkrai, S., Shen, C., & van den Hengel, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298794 · Cited by 385
Metadata
- Authors
- Teo, C. H., Smola, A., Vishwanathan, S. V. N., & Le, Q. V.
- Venue
- Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2007-08-12
- Pages
- 727-736
- OpenAlex
- W2005688170
- Cited by
- 171
- References
- 2
- FWCI
- 12.79
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A scalable modular convex solver for regularized risk minimization. https://app.cochat.ai/papers/10.1145/1281192.1281270
- The paper
- Teo, C. H., Smola, A., Vishwanathan, S. V. N., & Le, Q. V. (2007). A scalable modular convex solver for regularized risk minimization. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1281192.1281270
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
