# Learning Over Dirty Data Without Cleaning

arXiv: https://arxiv.org/abs/2004.02308 · HTML: https://mlsanity.com/paper/2004.02308/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines](https://mlsanity.com/paper/2302.04702/) (2023)
- [In-Database Data Imputation](https://mlsanity.com/paper/2401.03359/) (2024)
- [Towards Consistent Language Models Using Declarative Constraints](https://mlsanity.com/paper/2312.15472/) (2023)
- [Certain and Approximately Certain Models for Statistical Learning](https://mlsanity.com/paper/2402.17926/) (2024)
- [Learning Over Dirty Data with Minimal Repairs](https://mlsanity.com/paper/2503.13921/) (2025)

## Abstract (arXiv)

Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Learning over dirty databases may result in inaccurate models. Users have to spend a great deal of time and effort to repair data errors and create a clean database for learning. Moreover, as the information required to repair these errors is not often available, there may be numerous possible clean versions for a dirty database. We propose DLearn, a novel relational learning system that learns directly over dirty databases effectively and efficiently without any preprocessing. DLearn leverages database constraints to learn accurate relational models over inconsistent and heterogeneous data. Its learned models represent patterns over all possible clean instances of the data in a usable form. Our empirical study indicates that DLearn learns accurate models over large real-world databases efficiently.