论文标题
合奏平方：元汽车系统
Ensemble Squared: A Meta AutoML System
论文作者
论文摘要
目前，有许多障碍可以防止非专家利用机器学习解决方案，从缺乏统计学习技术的直觉到超参数调谐的棘手。这样的障碍导致对自动化机器学习（AUTOML）的兴趣爆炸，因此，现成的系统可以照顾最终用户的许多步骤，而无需在机器学习方面具有专业知识。本文介绍了合奏平方（Ensemble $^2 $），这是一种结合最新开源汽车系统结果的汽车系统。 Ensemble $^2 $通过利用其模型搜索空间和启发式方法的差异来利用现有汽车系统的多样性。从经验上讲，我们表明每个汽车系统的多样性足以证明在汽车系统级别上结合的合理性。在证明这一点时，我们还基于OpenML表格分类基准建立了新的最先进的汽车结果。
There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning. Such barriers have led to an explosion of interest in automated machine learning (AutoML), whereby an off-the-shelf system can take care of many of the steps for end-users without the need for expertise in machine learning. This paper presents Ensemble Squared (Ensemble$^2$), an AutoML system that ensembles the results of state-of-the-art open-source AutoML systems. Ensemble$^2$ exploits the diversity of existing AutoML systems by leveraging the differences in their model search space and heuristics. Empirically, we show that diversity of each AutoML system is sufficient to justify ensembling at the AutoML system level. In demonstrating this, we also establish new state-of-the-art AutoML results on the OpenML tabular classification benchmark.
