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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.10763 |
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| _version_ | 1866929708177817600 |
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| author | Mehta, Ronak Diakonikolas, Jelena Harchaoui, Zaid |
| author_facet | Mehta, Ronak Diakonikolas, Jelena Harchaoui, Zaid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization Mehta, Ronak Diakonikolas, Jelena Harchaoui, Zaid Machine Learning Optimization and Control 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. |
| title | Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2403.10763 |