Online Active Linear Regression via Thresholding
Online Active Linear Regression via Thresholding
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DOI：
10.1609/aaai.v31i1.10859
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
C. Riquelme;Ramesh Johari;Baosen Zhang
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文献类型：
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作者：
C. Riquelme;Ramesh Johari;Baosen Zhang
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 settings. Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality — significantly reducing both the mean and variance of the squared error.
