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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.09418 |
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| _version_ | 1866916895737774080 |
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| author | Anjum, Usman Stockman, Chris Luong, Cat Zhan, Justin |
| author_facet | Anjum, Usman Stockman, Chris Luong, Cat Zhan, Justin |
| contents | This paper introduces Domain Generalization Sharpness-Aware Minimization Model-Agnostic Meta-Learning (DGS-MAML), a novel meta-learning algorithm designed to generalize across tasks with limited training data. DGS-MAML combines gradient matching with sharpness-aware minimization in a bi-level optimization framework to enhance model adaptability and robustness. We support our method with theoretical analysis using PAC-Bayes and convergence guarantees. Experimental results on benchmark datasets show that DGS-MAML outperforms existing approaches in terms of accuracy and generalization. The proposed method is particularly useful for scenarios requiring few-shot learning and quick adaptation, and the source code is publicly available at GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Generalization to Improve Learning in Meta-Learning Algorithms Anjum, Usman Stockman, Chris Luong, Cat Zhan, Justin Machine Learning Artificial Intelligence This paper introduces Domain Generalization Sharpness-Aware Minimization Model-Agnostic Meta-Learning (DGS-MAML), a novel meta-learning algorithm designed to generalize across tasks with limited training data. DGS-MAML combines gradient matching with sharpness-aware minimization in a bi-level optimization framework to enhance model adaptability and robustness. We support our method with theoretical analysis using PAC-Bayes and convergence guarantees. Experimental results on benchmark datasets show that DGS-MAML outperforms existing approaches in terms of accuracy and generalization. The proposed method is particularly useful for scenarios requiring few-shot learning and quick adaptation, and the source code is publicly available at GitHub. |
| title | Domain-Generalization to Improve Learning in Meta-Learning Algorithms |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.09418 |