用最优传输度量数据分布相似性，自动为无监督表格任务选模型。
Automated Machine Learning for Unsupervised Tabular Tasks
- 基于最优传输计算无标签表格数据的分布相似性
- 统一方法在异常检测与聚类任务上表现优于强基线
- 适合需要自动化无监督建模的工业场景
本文提出LOTUS（Learning to Learn with Optimal Transport for Unsupervised Scenarios），一种简单有效的无监督机器学习模型选择方法，适用于异常检测与聚类等任务。核心思想是：若一个机器学习流程在分布相似的数据集上表现良好，则其在新数据集上也更可能表现优异。通过最优传输距离度量无标签表格数据间的分布相似性，进而推荐最适配的机器学习流程。实验表明，该方法在两项下游无监督任务上超越多个强基线，验证了其在多任务无监督建模中的潜力。
原文摘要 · Abstract (English)
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.
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