ContinualAI Reading Group: "Adaptation Strategies for Automated Machine Learning on Evolving Data"

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Vincenzo Lomonaco

Mar 10, 2021, 12:10:32 PM3/10/21
to Continual Learning & AI News
Dear All,

This Friday 12-03-2021, 5.30pm CET, for the ContinualAI Reading Group, Bilge Celik (Eindhoven University) will present 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 real-world and synthetic data streams with different types of concept drift. Based on this analysis, we propose ways to develop more sophisticated and robust AutoML techniques.

The event will be moderated by: Vincenzo Lomonaco.

- Eventbrite event (to save it in you calendar and get reminders):
- Microsoft Teamsclick here to join
- YouTube recordings of the previous sessions

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You can also contact me if you want to speak at one of the next sessions!

Best regards,

ContinualAI President
Vincenzo Lomonaco
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