# GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling

arXiv: https://arxiv.org/abs/2508.13653 · HTML: https://mlsanity.com/paper/2508.13653/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training](https://mlsanity.com/paper/2103.00123/) (2021)
- [GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning](https://mlsanity.com/paper/2012.10630/) (2020)
- [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks](https://mlsanity.com/paper/2409.12255/) (2024)
- [DRoP: Distributionally Robust Data Pruning](https://mlsanity.com/paper/2404.05579/) (2024)
- [Column Subset Selection and Nystr"om Approximation via Continuous Optimization](https://mlsanity.com/paper/2304.09678/) (2023)

## Built on by (most cited first, 2 of 2)

- [Market-Driven Subset Selection for Budgeted Training](https://mlsanity.com/paper/2510.02456/) (2025)
- [SAGE: Streaming Agreement-Driven Gradient Sketches for Representative Subset Selection](https://mlsanity.com/paper/2510.02470/) (2025)

## Abstract (arXiv)

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 CO2 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.