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PolieDRO: a novel classification and regression framework with non-parametric data-driven regularization.
- Source :
- Machine Learning; Aug2024, Vol. 113 Issue 8, p5807-5846, 40p
- Publication Year :
- 2024
-
Abstract
- PolieDRO is a novel analytics framework for classification and regression that harnesses the power and flexibility of data-driven distributionally robust optimization (DRO) to circumvent the need for regularization hyperparameters. Recent literature shows that traditional machine learning methods such as SVM and (square-root) LASSO can be written as Wasserstein-based DRO problems. Inspired by those results we propose a hyperparameter-free ambiguity set that explores the polyhedral structure of data-driven convex hulls, generating computationally tractable regression and classification methods for any convex loss function. Numerical results based on 100 real-world databases and an extensive experiment with synthetically generated data show that our methods consistently outperform their traditional counterparts. [ABSTRACT FROM AUTHOR]
- Subjects :
- ROBUST optimization
MACHINE learning
CONVEX functions
DATA analytics
CLASSIFICATION
Subjects
Details
- Language :
- English
- ISSN :
- 08856125
- Volume :
- 113
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Machine Learning
- Publication Type :
- Academic Journal
- Accession number :
- 178953594
- Full Text :
- https://doi.org/10.1007/s10994-024-06544-9