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Learning with limited supervision
November 1, 2017 @ 6:30 pm - 8:30 pm PDT
Abstract: 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. I will first discuss and formally prove some limitations of existing training criteria used for learning hierarchical generative models. I will then introduce novel architectures and methods to overcome these limitations, allowing us to learn a hierarchy of interpretable features from unalebeld data. Finally, I will discuss ways to use prior knowledge (such as physics laws or simulators) to provide weak forms of supervision, showing how we can learn to solve useful tasks, including object tracking, without any labeled data.
Biography: Stefano Ermon is currently an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory. He completed his PhD in computer science at Cornell in 2015. His research interests include techniques for scalable and accurate inference in graphical models, 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’s research has won several awards, including three Best Paper Awards, a World Bank Big Data Innovation Challenge, and was selected by Scientific American as one of the 10 World Changing Ideas in 2016. He is a recipient of the Sony Faculty Innovation Award and NSF CAREER Award.