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Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning

Chelsea Finn · Sergey Levine

Hall A


Tl;dr: We will provide a unified perspective of how a variety of meta-learning algorithms enable learning from small datasets, an overview of applications where meta-learning can and cannot be easily applied, and a discussion of the outstanding challenges and frontiers of this sub-field. Abstract: In recent years, high-capacity models, such as deep neural networks, have enabled very powerful machine learning techniques in domains where data is plentiful. However, domains where data is scarce have proven challenging for such methods because high-capacity function approximators critically rely on large datasets for generalization. This can pose a major challenge for domains ranging from supervised medical image processing to reinforcement learning where real-world data collection (e.g., for robots) poses a major logistical challenge. Meta-learning or few-shot learning offers a potential solution to this problem: by learning to learn across data from many previous tasks, few-shot meta-learning algorithms can discover the structure among tasks to enable fast learning of new tasks. The objective of this tutorial is to provide a unified perspective of meta-learning: teaching the audience about modern approaches, describing the conceptual and theoretical principles surrounding these techniques, presenting where these methods have been applied previously, and discussing the fundamental open problems and challenges within the area. We hope that this tutorial is useful for both machine learning researchers whose expertise lies in other areas, while also providing a new perspective to meta-learning researchers. All in all, we aim to provide audience members with the ability to apply meta-learning to their own applications, and develop new meta-learning algorithms and theoretical analyses driven by the current challenges and limitations of existing work.

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