Poster
Active Learning for Non-Parametric Regression Using Purely Random Trees
Jack Goetz · Ambuj Tewari · Paul Zimmerman

Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #129

Active learning is the task of using labelled data to select additional points to label, with the goal of fitting the most accurate model with a fixed budget of labelled points. In binary classification active learning is known to produce faster rates than passive learning for a broad range of settings. However in regression restrictive structure and tailored methods were previously needed to obtain theoretically superior performance. In this paper we propose an intuitive tree based active learning algorithm for non-parametric regression with provable improvement over random sampling. When implemented with Mondrian Trees our algorithm is tuning parameter free, consistent and minimax optimal for Lipschitz functions.

Author Information

Jack Goetz (University of Michigan)
Ambuj Tewari (University of Michigan)
Paul Zimmerman (University of Michigan)

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