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NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
Kevin G Jamieson · Lalit Jain · Chris Fernandez · Nicholas J. Glattard · Rob Nowak

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #28

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for real-world, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

Author Information

Kevin G Jamieson (University of Wisconsin)
Lalit Jain (University of Wisconsin)
Chris Fernandez (University of Wisconsin)
Nicholas J. Glattard (University of Wisconsin)
Rob Nowak (Wisconsin)

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