MODE: Multi-Objective Adaptive Coreset Selection
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915693427949568 |
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| author | Mukherjee, Tanmoy Marquis, Pierre Bouraoui, Zied |
| author_facet | Mukherjee, Tanmoy Marquis, Pierre Bouraoui, Zied |
| contents | We present Mode(Multi-Objective adaptive Data Efficiency), a framework that dynamically combines coreset selection strategies based on their evolving contribution to model performance. Unlike static methods, \mode adapts selection criteria to training phases: emphasizing class balance early, diversity during representation learning, and uncertainty at convergence. We show that MODE achieves (1-1/e)-approximation with O(n \log n) complexity and demonstrates competitive accuracy while providing interpretable insights into data utility evolution. Experiments show \mode reduces memory requirements |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21152 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MODE: Multi-Objective Adaptive Coreset Selection Mukherjee, Tanmoy Marquis, Pierre Bouraoui, Zied Machine Learning Artificial Intelligence We present Mode(Multi-Objective adaptive Data Efficiency), a framework that dynamically combines coreset selection strategies based on their evolving contribution to model performance. Unlike static methods, \mode adapts selection criteria to training phases: emphasizing class balance early, diversity during representation learning, and uncertainty at convergence. We show that MODE achieves (1-1/e)-approximation with O(n \log n) complexity and demonstrates competitive accuracy while providing interpretable insights into data utility evolution. Experiments show \mode reduces memory requirements |
| title | MODE: Multi-Objective Adaptive Coreset Selection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.21152 |