English(EN) Active Learning with Low-Rank Structure for Data Selection

新框架使用低秩近似进行高效机器学习数据选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一个新颖的数据选择框架，该框架利用低秩近似和基于残差的采样。这种方法旨在识别一小部分代表性数据以进行高效的机器学习模型训练。与以往依赖几何结构和聚类的方法不同，这个新框架利用了许多现代数据集中固有的全局代数结构。理论保证和经验评估表明，其性能优于现有策略。 AI

影响这项研究提供了一种更有效的数据选择训练方法，有可能降低计算成本并提高模型性能。

排序理由该集群包含一篇详细介绍新机器学习方法的学术论文。

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arXiv cs.LG TIER_1 English(EN) · Vincent Cohen-Addad, Sasidhar Kunapuli, Vahab Mirrokni, Mahdi Nikdan, David P. Woodruff, Samson Zhou · 2026-06-16 04:00

低秩结构的主动学习用于数据选择

arXiv:2606.16045v1 Announce Type: new Abstract: In the data selection problem, the objective is to choose a small, representative subset of data that can be used to efficiently train a machine learning model. Sener and Savarese [ICLR 2018] showed that, given an embedding represen…