Corpus ID: 226221870

Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference

@article{Liu2020PicketGA,
  title={Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference},
  author={Zifan Liu and Zhechun Zhou and Theodoros Rekatsinas},
  journal={arXiv: Learning},
  year={2020}
}
Data corruption is an impediment to modern machine learning deployments. Corrupted data can severely bias the learned model and can also lead to invalid inferences. We present, Picket, a simple framework to safeguard against data corruptions during both training and deployment of machine learning models over tabular data. For the training stage, Picket identifies and removes corrupted data points from the training data to avoid obtaining a biased model. For the deployment stage, Picket flags… Expand

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