English(EN) Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization

新算法ReSGD通过可学习的先验增强多源数据选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一种新颖的随机梯度算法ReSGD，旨在改进多源双层数据选择。该方法利用可学习的分类先验来参数化概率分布，实现了无损舍入方案，并避免了先前技术中不稳定的梯度估计等问题。实验表明，ReSGD在选择可行数据子集方面非常有效，尤其是在弱次模条件下。 AI

影响为机器学习任务中的数据选择优化引入了一种新方法。

排序理由该集群包含一篇详细介绍新算法及其实验验证的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Shizhan Cai, Qixin Zhang, Siyuan Ma, Siyuan Liang, Yongcheng Jing, Dacheng Tao · 2026-10-15 04:00

通过可学习的分类先验进行概率多源数据选择：方差缩减与保证的去随机化

arXiv:2610.14942v1 Announce Type: cross Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem is to relax the original bilevel combinatorial optimization into a continuous …