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Abstract:
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In supervised learning problems, ``Active Learning" refers to the iterative process of sequential data selection and model building, with the goal of building a better model while requiring fewer observations. From a statistical viewpoint, sequential design of experiments seeks to solve a similar problem, but often focuses on parametric models such as linear regression. We consider the challenge of Active Learning with very flexible models. In order to meet this challenge, a framework for formal statistical inference must be available for the model. We consider Bayesian Additive Regression Trees, a flexible ensemble model which can deal with high dimensionality, irrelevant predictors, nonlinear relationships, interactions, and local effects. We will discuss issues involved in the development of BART as an active learning tool, including computation and comparisons with other methods.
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