新理论将次模函数保证扩展到非正值情况

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 15:42

研究人员开发了一个新的理论框架来理解次模函数，这在机器学习中对于模拟收益递减至关重要。这种新方法将“曲率”概念扩展到可以处理负值的函数，这是先前方法中的一个限制。所提出的带有剪枝的贪心算法为任何次模函数提供了曲率控制的乘法保证，这标志着在需要非负性或单调性的现有界限之外取得了重大进展。 AI

影响为机器学习中常见的优化问题引入了更鲁棒的理论框架，有可能提高实验设计和特征选择等任务的性能。

排序理由详细介绍次模函数新理论框架和算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Alan Kuhnle · 2026-05-08 15:42

曲率超越正性：任意次模函数贪心保证

Submodular functions -- functions exhibiting diminishing returns -- are central to machine learning. When the objective is monotone and non-negative, the greedy algorithm achieves a tight $63\%$ approximation. But many practical objectives incorporate costs that make them negativ…