通过端到端最大化KL散度学习异常子群
Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence
AI总结：
本文提出端到端可优化方法Syflow，利用归一化流建模任意目标分布并引入可解释神经层，以克服现有子群发现方法在离散化、目标分布、扩展性和多样性方面的局限，可靠发现高度异常且可解释的子群。
摘要：
寻找并描述在目标属性上表现异常的子群体，在从人口普查数据中识别弱势人口群体到在金纳米颗粒中发现导电分子等众多科学领域中具有重要应用。当前寻找此类子群的方法需要预先离散化的预测变量，不允许非平凡的目标分布，无法扩展到大型数据集，并且难以找到多样化的结果。为了解决这些局限性，我们提出了Syflow，这是一种端到端可优化的方法，其中我们利用归一化流来建模任意目标分布，并引入了一种新颖的神经层，该层能够产生易于解释的子群描述。我们在合成数据和真实世界数据（包括一项案例研究）上证明，Syflow能够可靠地找到高度异常的子群，并伴随有洞察力的描述。
英文摘要：
Finding and describing sub-populations that are exceptional regarding a target property has important applications in many scientific disciplines, from identifying disadvantaged demographic groups in census data to finding conductive molecules within gold nanoparticles. Current approaches to finding such subgroups require pre-discretized predictive variables, do not permit non-trivial target distributions, do not scale to large datasets, and struggle to find diverse results. To address these limitations, we propose Syflow, an end-to-end optimizable approach in which we leverage normalizing flows to model arbitrary target distributions, and introduce a novel neural layer that results in easily interpretable subgroup descriptions. We demonstrate on synthetic and real-world data, including a case study, that Syflow reliably finds highly exceptional subgroups accompanied by insightful descriptions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
