Automatic and Precise Data Validation for Machine Learning

Author:

Shankar Shreya1ORCID,Fawaz Labib2ORCID,Gyllstrom Karl2ORCID,Parameswaran Aditya1ORCID

Affiliation:

1. University of California, Berkeley, Berkeley, CA, USA

2. Meta, Menlo Park, CA, USA

Funder

NSF

NDSEG

Publisher

ACM

Reference52 articles.

1. Detecting data errors

2. Samuel Ackerman , Eitan Farchi , Orna Raz , Marcel Zalmanovici , and Parijat Dube . 2020. Detection of data drift and outliers affecting machine learning model performance over time. arXiv preprint arXiv:2012.09258 ( 2020 ). Samuel Ackerman, Eitan Farchi, Orna Raz, Marcel Zalmanovici, and Parijat Dube. 2020. Detection of data drift and outliers affecting machine learning model performance over time. arXiv preprint arXiv:2012.09258 (2020).

3. Samaneh Aminikhanghahi and Diane J Cook . 2017. A survey of methods for time series change point detection. Knowledge and information systems , Vol. 51 , 2 ( 2017 ), 339--367. Samaneh Aminikhanghahi and Diane J Cook. 2017. A survey of methods for time series change point detection. Knowledge and information systems, Vol. 51, 2 (2017), 339--367.

4. Fabrizio Angiulli and Clara Pizzuti. 2002. Fast Outlier Detection in High Dimensional Spaces. In PKDD. Fabrizio Angiulli and Clara Pizzuti. 2002. Fast Outlier Detection in High Dimensional Spaces. In PKDD.

5. TFX

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