In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsupervised machine learning(ML) tasks such as outlier detection and clustering. Our intuition behind this work is that a machine learning pipeline will perform well in a new dataset if it previously worked well on datasets with a similar underlying data distribution. We use Optimal Transport distances to find this similarity between unlabeled tabular datasets and recommend machine learning pipelines with one unified single method on two downstream unsupervised tasks: outlier detection and clustering. We present the effectiveness of our approach with experiments against strong baselines and show that LOTUS is a very promising first step toward model selection for multiple unsupervised ML tasks.

翻译：本文提出LOTUS（面向无监督场景的基于最优传输的元学习方法），这是一种为离群点检测与聚类等多种无监督机器学习任务进行模型选择的简洁而有效的方法。本研究的核心思想是：若某个机器学习流程在具有相似底层数据分布的数据集上表现良好，则其在新数据集上也可能取得优异性能。我们采用最优传输距离度量未标注表格数据集之间的相似性，并基于此通过统一的单一方法为离群点检测与聚类这两个下游无监督任务推荐机器学习流程。通过对比强基线的实验验证，我们证明了该方法的有效性，并表明LOTUS为面向多元无监督机器学习任务的模型选择研究迈出了极具前景的第一步。