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Nov, 2023
公平瓦瑟斯坦核心集
Fair Wasserstein Coresets
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Zikai Xiong, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
TL;DR
通过生成公平的合成代表样本及样本级权重来最小化原始数据集与加权合成样本之间的Wasserstein距离,并通过线性约束实施(实证版本的)人口平等,从而在下游学习任务中提供公平的聚类算法,其竞争性能优于现有的公平聚类方法,即使通过公平预处理技术来增强后者的公平性。
Abstract
Recent technological advancements have given rise to the ability of collecting vast amounts of data, that often exceed the capacity of commonly used
machine learning
algorithms. Approaches such as
coresets
and
→