GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912547995648000 |
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| author | Jha, Ashish Phan, Anh huy Dibo, Razan Leplat, Valentin |
| author_facet | Jha, Ashish Phan, Anh huy Dibo, Razan Leplat, Valentin |
| contents | Training modern neural networks on large datasets is computationally and environmentally costly. We introduce GRAFT, a scalable in-training subset selection method that (i) extracts a low-rank feature representation for each batch, (ii) applies a Fast MaxVol sampler to select a small, diverse subset that spans the batch's dominant subspace, and (iii) dynamically adjusts the subset size using a gradient-approximation criterion. By operating in low-rank subspaces and training on carefully chosen examples instead of full batches, GRAFT preserves the training trajectory while reducing wall-clock time, energy consumption, and $\mathrm{CO}_2$ emissions. Across multiple benchmarks, GRAFT matches or exceeds recent selection baselines in both accuracy and efficiency, providing a favorable trade-off between accuracy, efficiency, and emissions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Jha, Ashish Phan, Anh huy Dibo, Razan Leplat, Valentin Machine Learning Artificial Intelligence Numerical Analysis Training modern neural networks on large datasets is computationally and environmentally costly. We introduce GRAFT, a scalable in-training subset selection method that (i) extracts a low-rank feature representation for each batch, (ii) applies a Fast MaxVol sampler to select a small, diverse subset that spans the batch's dominant subspace, and (iii) dynamically adjusts the subset size using a gradient-approximation criterion. By operating in low-rank subspaces and training on carefully chosen examples instead of full batches, GRAFT preserves the training trajectory while reducing wall-clock time, energy consumption, and $\mathrm{CO}_2$ emissions. Across multiple benchmarks, GRAFT matches or exceeds recent selection baselines in both accuracy and efficiency, providing a favorable trade-off between accuracy, efficiency, and emissions. |
| title | GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling |
| topic | Machine Learning Artificial Intelligence Numerical Analysis |
| url | https://arxiv.org/abs/2508.13653 |