[ContinualAI Reading Group] Adaptation Strategies for Automated Machine Learning on Evolving Data

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 .

:pushpin: Eventbrite event : Adaptation Strategies for Automated Machine Learning on Evolving Data Tickets, Fri, Mar 12, 2021 at 5:30 PM | Eventbrite
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:pushpin: YouTube Recording: https://youtu.be/NPwrg1rudcY
:pushpin: Slides: Adaptation Strategies for Automated Machine Learning on Evolving Data - Google Slides

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