{"ID":2825491,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21152","arxiv_id":"2512.21152","title":"MODE: Multi-Objective Adaptive Coreset Selection","abstract":"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","short_abstract":"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 representa...","url_abs":"https://arxiv.org/abs/2512.21152","url_pdf":"https://arxiv.org/pdf/2512.21152v1","authors":"[\"Tanmoy Mukherjee\",\"Pierre Marquis\",\"Zied Bouraoui\"]","published":"2025-12-24T12:43:40Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}