ContinualAI RG: “Adaptation Strategies for Automated Machine Learning on Evolving Data“

This Friday 12-03-2021, CET, for the ContinualAI Reading Group, Bilge Celik (Eindhoven University) presented the paper: Title: “Adaptation Strategies for Automated Machine Learning on Evolving Data“ Abstract: Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of data stream challenges such as concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches. We do this for a variety of AutoML approaches for building machine learning pipelines, including those that leverage Bayesian optimization, genetic programming, and random search with automated stacking. These are evaluated empirically on rea