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Learning with limited supervision

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Many of the recent successes of machine learning have been characterized by the availability of large quantities of labeled data. Nonetheless, we observe that humans are often able to learn with very few labeled examples or with only high level instructions for how a task should be performed. In this talk, I will present some new approaches for learning useful models in contexts where labeled training data is scarce or not available at all. 1) I will introduce new techniques for learning generative models, including the use of random projections to simplify probabilistic models while preserving most of the information, and a new boosting framework to learn ensembles of models. 2) I will discuss ways to use prior knowledge (such as physical laws) to provide supervision, showing how we can learn to solve useful tasks, including object tracking, without any labeled data. 3) Finally, I will introduce new approaches to leverage spatio-temporal structure in semi-supervised learning frameworks. I will present applications of these ideas to address development and sustainability issues, including new scalable methods to map poverty and monitor food security in developing countries using satellite imagery.

Bio: Stefano Ermon is an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and the Woods Institute for the Environment. He completed his PhD in computer science at Cornell in 2015. His research interests include techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. Stefano has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship.

This talk is part of the Machine Learning @ CUED series.

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