mlsanityData to 7 Oct 2026

HoloClean: Holistic Data Repairs with Probabilistic Inference

arXiv 1702.00820 ↗First posted 2 Feb 2017cs.DBcited by 79

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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.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2017.

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Citing papers per year: 2021: 9; 2022: 11; 2023: 19; 2024: 9; 2025: 16; 2026: 15.

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  2. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  3. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
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  5. 2021VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decompositioncited by 25
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  8. 2023Jellyfish: A Large Language Model for Data Preprocessingcited by 20
  9. 2025Exploring LLM Agents for Cleaning Tabular Machine Learning Datasetscited by 20
  10. 2022Saga: A Platform for Continuous Construction and Serving of Knowledge At Scalecited by 18
  11. 2022Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scalecited by 13
  12. 2023Construction of Knowledge Graphs: State and Challengescited by 12
38 more
  1. 2023REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelinescited by 10
  2. 2023Traffic-Domain Video Question Answering with Automatic Captioningcited by 10
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  4. 2023Data Cleaning and Machine Learning: A Systematic Literature Reviewcited by 9
  5. 2021Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Searchcited by 8
  6. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  7. 2023Lingua Manga: A Generic Large Language Model Centric System for Data Curationcited by 8
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  9. 2024UniDM: A Unified Framework for Data Manipulation with Large Language Modelscited by 7
  10. 2024Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuningcited by 7
  11. 2022DiffML: End-to-end Differentiable ML Pipelinescited by 6
  12. 2023DataVinci: Learning Syntactic and Semantic String Repairscited by 6
  13. 2025LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Visioncited by 6
  14. 2022Differentiable and Scalable Generative Adversarial Models for Data Imputationcited by 5
  15. 2023Desbordante: from benchmarking suite to high-performance science-intensive data profiler (preprint)cited by 5
  16. 2022Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experimentscited by 4
  17. 2022PInKS: Preconditioned Commonsense Inference with Minimal Supervisioncited by 4
  18. 2022Certifiable Robustness for Nearest Neighbor Classifierscited by 3
  19. 2022SHiFT: An Efficient, Flexible Search Engine for Transfer Learningcited by 3
  20. 2024In-Database Data Imputationcited by 3
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  22. 2026PrepBench: How Far Are We from Natural-Language-Driven Data Preparation?cited by 3
  23. 2021A probabilistic database approach to autoencoder-based data cleaningcited by 2
  24. 2023KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Sciencecited by 2
  25. 2023Naive Bayes Classifiers over Missing Data: Decision and Poisoningcited by 2
  26. 2023BClean: A Bayesian Data Cleaning Systemcited by 2
  27. 2025LLM-Aided Customizable Profiling of Code Data Based On Programming Language Conceptscited by 2
  28. 2021Towards Artificial Intelligence Enabled Financial Crime Detectioncited by 1
  29. 2022Repairing Systematic Outliers by Learning Clean Subspaces in VAEscited by 1
  30. 2023DataAssist: A Machine Learning Approach to Data Cleaning and Preparationcited by 1
  31. 2023Towards Consistent Language Models Using Declarative Constraintscited by 1
  32. 2024Foundation Model Engineering: Engineering Foundation Models Just as Engineering Softwarecited by 1
  33. 2025Learning High-dimensional Gaussians from Censored Datacited by 1
  34. 2025Refining Labeling Functions with Limited Labeled Datacited by 1
  35. 2025Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Modelscited by 1
  36. 2025Towards Cross-Modal Error Detection with Tables and Imagescited by 1
  37. 2025Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysiscited by 1
  38. 2025Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation?cited by 1

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