AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Online Active Linear Regression via Thresholding
Carlos Riquelme, Ramesh Johari, Baosen Zhang

Last modified: 2017-02-13


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.


Active Learning, Linear Regression, Reinforcement Learning, Machine Learning, Online Algorithms

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