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Learning through Exploration
This video was recorded at 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Washington 2010. This tutorial is about learning through exploration. The goal is to learn how to make decisions in partial feedback settings where an agent repeatedly observes some information, chooses an action, and then learns how this action paid off (but doesn't get to see how other actions would have paid off). We plan to cover all aspects of this general problem: learning, evaluation, limitations of ability to learn in this setting, and the relationship to traditional supervised learning.
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- Mobile Compatibility:
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- English
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
