Sequential Learning for Personalization; Tutorial on Meta-Learning

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Thanks to Motion.Lab for hosting us! There will be drinks available.

Talk 1: Sequential Learning for Personalization

Speaker: Prof. Dr. Maurits Kaptein

Abstract: In this talk Maurits will detail the main data science challenges encountered when trying to learn “the right treatment, for the right person, at the right time” based on data. Using a Multi-Armed Bandit formalization of personalization Maurits will discuss novel policies (e.g., Bootstrap Thompson Sampling), software (https://github.com/Nth-iteration-labs/streamingbandit & https://github.com/Nth-iteration-labs/contextual), and offline policy evaluation methods to effectively address personalization problems.

Bio: Prof. Dr. Maurits Kaptein is the PI of the Computational Personalization lab at JADS research; he and his team work on (statistical) methods for treatment personalization. http://www.mauritskaptein.com/about/

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Talk 2: Learn how to learn how to learn: A tutorial on Meta-learning

Speaker: Joaquin Vanschoren

Abstract: When we learn new skills, we rarely - if ever - start from scratch. We start from skills learned earlier in related tasks, reuse approaches that worked well before, and focus on what is likely worth trying based on experience. With every skill learned, learning new skills becomes easier, requiring fewer examples and less trial-and-error. In short, we 'learn how to learn' across tasks. Likewise, when building machine learning models for a specific task, we often build on experience with related tasks, or use our (often implicit) understanding of the behavior of machine learning techniques to help make the right choices.

Meta-learning is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this tutorial, we provide an overview of the state of the art in this fascinating and continuously evolving field.

[This is a shorter version of the Automatic Machine Learning tutorial given at NIPS 2018.]

Bio: Joaquin Vanschoren is an Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.