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

arXiv: https://arxiv.org/abs/2006.04730 · HTML: https://mlsanity.com/paper/2006.04730/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 11 of 11)

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- [BClean: A Bayesian Data Cleaning System](https://mlsanity.com/paper/2311.06517/) (2023)
- [Repairing Systematic Outliers by Learning Clean Subspaces in VAEs](https://mlsanity.com/paper/2207.08050/) (2022)
- [Foundation Model Engineering: Engineering Foundation Models Just as Engineering Software](https://mlsanity.com/paper/2407.08176/) (2024)
- [Quality Assessment of Tabular Data using Large Language Models and Code Generation](https://mlsanity.com/paper/2509.10572/) (2025)
- [Quality Issues in Machine Learning Software Systems](https://mlsanity.com/paper/2306.15007/) (2023)
- [Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics](https://mlsanity.com/paper/2412.06265/) (2024)
- [Backdoor attacks on DNN and GBDT -- A Case Study from the insurance domain](https://mlsanity.com/paper/2412.08366/) (2024)

## Abstract (arXiv)

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, in an online manner, corrupted query points to a trained machine learning model that due to noise will result in incorrect predictions. To detect corrupted data, Picket uses a self-supervised deep learning model for mixed-type tabular data, which we call PicketNet. To minimize the burden of deployment, learning a PicketNet model does not require any human-labeled data. Picket is designed as a plugin that can increase the robustness of any machine learning pipeline. We evaluate Picket on a diverse array of real-world data considering different corruption models that include systematic and adversarial noise during both training and testing. We show that Picket consistently safeguards against corrupted data during both training and deployment of various models ranging from SVMs to neural networks, beating a diverse array of competing methods that span from data quality validation models to robust outlier-detection models.