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Tutorial
Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning
Chelsea Finn · Sergey Levine

Mon Jun 10 01:00 PM -- 03:15 PM (PDT) @ Hall A
in Tutorials Hall A »
Event URL: https://sites.google.com/view/icml19metalearning »

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.

[ Slides]  [ Video] 

Author Information

Chelsea Finn (Stanford, Google, UC Berkeley)
Chelsea Finn

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.

Sergey Levine (UC Berkeley)
Sergey Levine

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.

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