Statistics > Machine Learning
arXiv:2403.10763 (stat)
[Submitted on 16 Mar 2024 (v1), last revised 11 Feb 2025 (this version, v2)]
Title:Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
View a PDF of the paper titled Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization, by Ronak Mehta and 2 other authors
View PDFHTML (experimental)Abstract:We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using $f$-DRO and spectral/$L$-risk minimization. We present Drago, a stochastic primal-dual algorithm that combines cyclic and randomized components with a carefully regularized primal update to achieve dual variance reduction. Owing to its design, Drago enjoys a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems with a fine-grained dependency on primal and dual condition numbers. Theoretical results are supported by numerical benchmarks on regression and classification tasks.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC) |
| Cite as: | arXiv:2403.10763 [stat.ML] |
| (orarXiv:2403.10763v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2403.10763 arXiv-issued DOI via DataCite |
Submission history
From: Ronak Mehta [view email][v1] Sat, 16 Mar 2024 02:06:14 UTC (5,583 KB)
[v2] Tue, 11 Feb 2025 17:28:34 UTC (1,114 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization, by Ronak Mehta and 2 other authors
Current browse context:
stat.ML
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.