MMLL webinar II: Recommenders, bandits, and causality
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The Mercury Machine Learning Lab (MMLL) would like to invite you to the MMLL online seminar series. In this series of four webinars, the lab will focus on causality, information retrieval, natural language processing, and reinforcement learning. |
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Program 15.00: Introduction of the Mercury Machine Learning Lab by Prof. dr. Frans Oliehoek (TU Delft) |
Abstract “Recommenders, bandits, and causality” Using the Netflix application as a motivating example, I will discuss the “recommendation” problem and some of its variants, from the lens of contextual bandits and causal inference. I will present some recent results, but mostly pose a number of research questions that are relevant to industrial applications. Short bio of Dr. Nikos Vlassis Nikos Vlassis is a Principal Scientist at Adobe Research. His research interests include Machine Learning and applications. In the past he has held positions as Senior Data Scientist at Netflix, Senior Data Scientist at Adobe Research, Principal Investigator at the Luxembourg Centre for Systems Biomedicine, Principal Scientist in OneTree Solutions, Assistant Professor at the Technical University of Crete, Greece, and Assistant Professor at the University of Amsterdam, The Netherlands. He received his diploma and PhD in Electrical and Computer Engineering from the National Technical University of Athens, Greece. He has served as an Action Editor of the Journal of Machine Learning Research during 2016-2020. |
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More info about this series This series of webinars will focus on causality, information retrieval, natural language processing, and reinforcement learning. Each event will be 1 hour with a short introduction (5 min), a 40-minute talk by the keynote speaker, and a 15 minute Q&A. The webinar is planned for the following dates: · April 21st 15:00-16:00 Prof. dr. Mounia Lalmas on information retrieval |