Browsing by Author "Lai, Henry"
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Item Toward Data-centric Automated Machine Learning(2023-04-14) Lai, Henry; Hu, XiaMachine learning has become increasingly popular and has shown significant success in many fields. There are four main processes involved in developing a machine learning solution: data preparation, model selection, hyper-parameter tuning, and deployment for feedback collection. While automated machine learning (AutoML) has been proposed to streamline the middle two processes and deliver efficient solutions without requiring laborious trial-and-error efforts, the framework requires a well-prepared dataset and a perfectly defined setting, which may limit its capability toward more challenging real-world applications. Recent studies suggest that data preparation is often the key to optimal solutions in many challenging real-world applications. To bridge the gap between model selection and data preparation, we propose a complimentary AutoML framework that focuses on data-centric operations, which perform automated data preparations in different stages of a machine learning pipeline. Our framework includes a data-centric model customization framework to generate sample-specific learning strategies based on the attributes of individual data samples, a data-centric knowledge acquisition framework to effectively collect expert knowledge based on data distribution while considering its long-term effects on the model training procedure, and a model-aware data preparation framework that takes data distribution and attributes into consideration to further improve the datasets for challenging problem settings. Our goal is to develop an end-to-end data-centric AutoML system for real-world applications. To achieve this, we propose developing an end-to-end AutoML system for anomaly detection on time series data as a prototype to promote the proposed framework. With all these efforts, our research could further expand the capability of AutoML toward real-world applications.