Polynomial time algorithms for dual volume sampling

@inproceedings{Li2017PolynomialTA,
  title={Polynomial time algorithms for dual volume sampling},
  author={Chengtao Li and Stefanie Jegelka and Suvrit Sra},
  booktitle={NIPS},
  year={2017}
}
We study dual volume sampling, a method for selecting k columns from an n⇥m short and wide matrix (n  k  m) such that the probability of selection is proportional to the volume spanned by the rows of the induced submatrix. This method was proposed by Avron and Boutsidis (2013), who showed it to be a promising method for column subset selection and its multiple applications. However, its wider adoption has been hampered by the lack of polynomial time sampling algorithms. We remove this… CONTINUE READING
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Warmuth . Unbiased estimates for linear regression via volume sampling

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