SLiMFast: Guaranteed Results for Data Fusion and Source Reliability

@article{Rekatsinas2017SLiMFastGR,
  title={SLiMFast: Guaranteed Results for Data Fusion and Source Reliability},
  author={Theodoros Rekatsinas and M. Joglekar and H. Garcia-Molina and Aditya G. Parameswaran and Christopher R{\'e}},
  journal={Proceedings of the 2017 ACM International Conference on Management of Data},
  year={2017}
}
  • Theodoros Rekatsinas, M. Joglekar, +2 authors Christopher Ré
  • Published 2017
  • Computer Science
  • Proceedings of the 2017 ACM International Conference on Management of Data
  • We focus on data fusion, i.e., the problem of unifying conflicting data from data sources into a single representation by estimating the source accuracies. We propose SLiMFast, a framework that expresses data fusion as a statistical learning problem over discriminative probabilistic models, which in many cases correspond to logistic regression. In contrast to previous approaches that use complex generative models, discriminative models make fewer distributional assumptions over data sources and… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Explore key concepts

    Links to highly relevant papers for key concepts in this paper:

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 34 CITATIONS, ESTIMATED 50% COVERAGE

    Snorkel: Rapid Training Data Creation with Weak Supervision

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Snorkel: rapid training data creation with weak supervision

    VIEW 1 EXCERPT
    CITES BACKGROUND

    TurboLift: fast accuracy lifting for historical data recovery

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Snuba: Automating Weak Supervision to Label Training Data

    VIEW 1 EXCERPT

    HomeRun: Scalable Sparse-Spectrum Reconstruction of Aggregated Historical Data

    VIEW 1 EXCERPT
    CITES BACKGROUND

    FILTER CITATIONS BY YEAR

    2016
    2020

    CITATION STATISTICS

    • 1 Highly Influenced Citations

    • Averaged 10 Citations per year from 2018 through 2020

    References

    Publications referenced by this paper.

    On discriminative vs

    • A. Y. Ng, M. I. Jordan
    • generative classifiers: A comparison of logistic regression and naive bayes. In NIPS, pages 841–848. MIT Press,
    • 2002
    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL