Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866915129916915712 |
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| author | Soma, Tasuku Gatmiry, Khashayar Gupta, Sharut Jegelka, Stefanie |
| author_facet | Soma, Tasuku Gatmiry, Khashayar Gupta, Sharut Jegelka, Stefanie |
| contents | Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems including group DRO, subpopulation fairness, and empirical conditional value at risk (CVaR) optimization. Our new algorithms achieve faster convergence rates than existing algorithms for multiple DRO settings. We also provide a new information-theoretic lower bound that implies our bounds are tight for group DRO. Empirically, too, our algorithms outperform known methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_13669 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond Soma, Tasuku Gatmiry, Khashayar Gupta, Sharut Jegelka, Stefanie Machine Learning Optimization and Control Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems including group DRO, subpopulation fairness, and empirical conditional value at risk (CVaR) optimization. Our new algorithms achieve faster convergence rates than existing algorithms for multiple DRO settings. We also provide a new information-theoretic lower bound that implies our bounds are tight for group DRO. Empirically, too, our algorithms outperform known methods. |
| title | Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2212.13669 |