论文标题
使用矩阵和张量分解的有效汽车管道搜索
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
论文作者
论文摘要
在新数据集上寻求良好监督学习模型的数据科学家可以做出许多选择：他们必须预处理数据，选择功能，可能降低维度，选择估计算法并为每个管道组件选择超参数。随着新的管道组件，选择数量的组合爆炸！在这项工作中，我们设计了一个新的汽车系统来应对此挑战：一个自动化系统，以设计监督的学习管道。我们的系统将矩阵和张量分解用作替代模型来对组合管道搜索空间进行建模。在这些模型下，我们开发了贪婪的实验设计协议，以有效地收集有关新数据集的信息。大型现实世界分类问题的实验证明了我们方法的有效性。
Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design a new AutoML system to address this challenge: an automated system to design a supervised learning pipeline. Our system uses matrix and tensor factorization as surrogate models to model the combinatorial pipeline search space. Under these models, we develop greedy experiment design protocols to efficiently gather information about a new dataset. Experiments on large corpora of real-world classification problems demonstrate the effectiveness of our approach.
