“Influence sketching”: Finding influential samples in large-scale regressions

@article{Wojnowicz2016InfluenceSF,
  title={“Influence sketching”: Finding influential samples in large-scale regressions},
  author={M. Wojnowicz and B. Cruz and X. Zhao and B. Wallace and M. Wolff and Jay Luan and Caleb Crable},
  journal={2016 IEEE International Conference on Big Data (Big Data)},
  year={2016},
  pages={3601-3612}
}
  • M. Wojnowicz, B. Cruz, +4 authors Caleb Crable
  • Published 2016
  • Computer Science, Mathematics
  • 2016 IEEE International Conference on Big Data (Big Data)
  • There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial attack. In order to solve the “needle in the haystack” problem of which samples to inspect, we develop a new scalable version of Cook's distance, a classical statistical technique for identifying samples which unusually strongly impact the fit of a regression… CONTINUE READING
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