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Active Learning: From Theory to Practice
Robert Nowak · Steve Hanneke

Mon Jun 10 01:00 PM -- 03:15 PM (PDT) @ Hall B
Event URL: http://nowak.ece.wisc.edu/ActiveML.html »

The field of Machine Learning has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text, but they must be trained with more images and text than a person can see in nearly a lifetime. Generating the necessary training data sets can require an enormous human effort. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative data for labeling so that human time is not wasted labeling irrelevant, redundant, or trivial examples. This tutorial will overview applications and provide an introduction to basic theory and algorithms for active machine learning. It will particularly focus on provably sound active learning algorithms and quantify the reduction of labeled training data required for learning.

Author Information

Robert Nowak (University of Wisconsion-Madison)
Robert Nowak

Robert Nowak holds the Nosbusch Professorship in Engineering at the University of Wisconsin-Madison, where his research focuses on signal processing, machine learning, optimization, and statistics.

Steve Hanneke (TTIC)
Steve Hanneke

Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute at Chicago. His research explores the theory of machine learning: designing new learning algorithms capable of learning from fewer samples, understanding the benefits and capabilities of interactive machine learning, developing new perspectives on transfer learning and life-long learning, and revisiting the basic probabilistic assumptions at the foundation of learning theory. Steve earned a Bachelor of Science degree in Computer Science from UIUC in 2005 and a Ph.D. in Machine Learning from Carnegie Mellon University in 2009 with a dissertation on the theoretical foundations of active learning.

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