Corpus ID: 126056120

On Computationally Tractable Selection of Experiments in Regression Models

@article{Wang2016OnCT,
  title={On Computationally Tractable Selection of Experiments in Regression Models},
  author={Yining Wang and Adams Wei Yu and Aarti Singh},
  journal={arXiv: Machine Learning},
  year={2016}
}
We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points. The primary focus is on linear regression models, while the technique extends to generalized linear models and Delta's method (estimating functions of linear regression models) as well. The algorithms are based on a continuous relaxation of an otherwise intractable combinatorial optimization problem, with sampling or greedy procedures as post-processing steps… Expand
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