Leveraged volume sampling for linear regression

@inproceedings{Derezinski2018LeveragedVS,
  title={Leveraged volume sampling for linear regression},
  author={Michal Derezi'nski and Manfred K. Warmuth and Daniel Hsu},
  year={2018}
}
Suppose an n ⇥ d design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to sample only a small number k ⌧ n of the responses, and then produce a weight vector whose sum of squares loss over all points is at most 1 + ✏ times the minimum. When k is very small (e.g., k = d), jointly sampling diverse subsets of points is crucial. One such method called volume sampling has a unique and desirable property that the… CONTINUE READING

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