Online Active Linear Regression via Thresholding

Authors

  • Carlos Riquelme Stanford University
  • Ramesh Johari Stanford University
  • Baosen Zhang University of Washington

DOI:

https://doi.org/10.1609/aaai.v31i1.10859

Keywords:

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

Abstract

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.

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Published

2017-02-13

How to Cite

Riquelme, C., Johari, R., & Zhang, B. (2017). Online Active Linear Regression via Thresholding. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10859