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Corpus ID: 2585460

Online Active Linear Regression via Thresholding

@inproceedings{Riquelme2017OnlineAL,
title={Online Active Linear Regression via Thresholding},
author={Carlos Riquelme and R. Johari and B. Zhang},
booktitle={AAAI},
year={2017}
}

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional… Expand