# CURED: Creating, Understanding, and Repairing Errors Demonstrator

arXiv: https://arxiv.org/abs/2607.20140 · HTML: https://mlsanity.com/paper/2607.20140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [MechDetect: Detecting Data-Dependent Errors](https://mlsanity.com/paper/2512.04138/) (2025)

## Abstract (arXiv)

Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demonstrator helps to bridge the gap between theoretical advancements and intuitive practical insights in the context of error models and data cleaning algorithms for tabular data. The demonstrator is available at https://cured.demo.calgo-lab.de/