# HoloClean: Holistic Data Repairs with Probabilistic Inference

arXiv: https://arxiv.org/abs/1702.00820 · HTML: https://mlsanity.com/paper/1702.00820/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2017.

## Built on by (most cited first, 30 of 79)

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- [DataVinci: Learning Syntactic and Semantic String Repairs](https://mlsanity.com/paper/2308.10922/) (2023)
- [LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision](https://mlsanity.com/paper/2509.22631/) (2025)
- [Differentiable and Scalable Generative Adversarial Models for Data Imputation](https://mlsanity.com/paper/2201.03202/) (2022)
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## Abstract (arXiv)

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic inference. HoloClean unifies existing qualitative data repairing approaches, which rely on integrity constraints or external data sources, with quantitative data repairing methods, which leverage statistical properties of the input data. Given an inconsistent dataset as input, HoloClean automatically generates a probabilistic program that performs data repairing. Inspired by recent theoretical advances in probabilistic inference, we introduce a series of optimizations which ensure that inference over HoloClean's probabilistic model scales to instances with millions of tuples. We show that HoloClean scales to instances with millions of tuples and find data repairs with an average precision of ~90% and an average recall of above ~76% across a diverse array of datasets exhibiting different types of errors. This yields an average F1 improvement of more than 2x against state-of-the-art methods.