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There is a considerable body of work on data cleaning which employs various principles to rectify erroneous data and transform a dirty dataset into a cleaner one.
An algorithm for fast recovery of sparse causal graphs
P. L. Spirtes and C. Glymour · 1991
Earlier work this paper cites.
Foundations of Databases
S. Abiteboul, R. Hull, and V. Vianu · 1995
Earlier work this paper cites.
Learning bayesian networks: Search methods and experimental results
M. Chickering, D. Geiger, and D. Heckerman · 1995
Earlier work this paper cites.
Using bayesian networks for cleansing trauma data
P. Doshi, L. G. Greenwald, and J. R. Clarke · 2003
Earlier work this paper cites.
Probabilistic noise identification and data cleaning
J. Kubica and A. W. Moore · 2003
Earlier work this paper cites.
The max-min hill-climbing bayesian network structure learning algorithm
I. Tsamardinos, L. E. Brown, and C. F. Aliferis · 2006
Earlier work this paper cites.
Conditional functional dependencies for data cleaning
P. Bohannon, W. Fan, F. Geerts, X. Jia, and A. Kementsietsidis · 2007
Earlier work this paper cites.
1 blog: Probabilistic models with unknown objects
B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov · 2007
Earlier work this paper cites.
Semandaq: a data quality system based on conditional functional dependencies
W. Fan, F. Geerts, and X. Jia · 2008
Earlier work this paper cites.
Conditional functional dependencies for capturing data inconsistencies
W. Fan, F. Geerts, X. Jia, and A. Kementsietsidis · 2008
Earlier work this paper cites.
Discovering conditional functional dependencies
W. Fan, F. Geerts, L. V. S. Lakshmanan, and M. Xiong · 2009
Earlier work this paper cites.
On approximating optimum repairs for functional dependency violations
S. Kolahi and L. V. S. Lakshmanan · 2009
Earlier work this paper cites.
Metric functional dependencies
N. Koudas, A. Saha, D. Srivastava, and S. Venkatasubramanian · 2009
Earlier work this paper cites.
A statistical method for integrated data cleaning and imputation
C. Mayfield, J. Neville, and S. Prabhakar · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol · 2010
Earlier work this paper cites.
Data cleaning and query answering with matching dependencies and matching functions
L. E. Bertossi, S. Kolahi, and L. V. S. Lakshmanan · 2011
Earlier work this paper cites.
Discovering conditional functional dependencies
W. Fan, F. Geerts, J. Li, and M. Xiong · 2011
Earlier work this paper cites.
Probabilistic Graphical Models: Principles and Techniques by daphne koller and nir friedman, MIT press, 1231 pp., $95.00, ISBN 0-262-01319-3
S. Parsons · 2011
Earlier work this paper cites.
Lightweight graphical models for selectivity estimation without independence assumptions
K. Tzoumas, A. Deshpande, and C. S. Jensen · 2011
Earlier work this paper cites.
Guided data repair
M. Yakout, A. K. Elmagarmid, J. Neville, M. Ouzzani, and I. F. Ilyas · 2011
Earlier work this paper cites.
Bayesian data cleaning for web data
Y. Hu, S. De, Y. Chen, and S. Kambhampati · 2012
Earlier work this paper cites.
A bayesian approach to discovering truth from conflicting sources for data integration
B. Zhao, B. I. P. Rubinstein, J. Gemmell, and J. Han · 2012
Earlier work this paper cites.
Holistic data cleaning: Putting violations into context
X. Chu, I. F. Ilyas, and P. Papotti · 2013
Cited alongside, same era.
NADEEF: a commodity data cleaning system
M. Dallachiesa, A. Ebaid, A. Eldawy, A. K. Elmagarmid, I. F. Ilyas, M. Ouzzani, and N. Tang · 2013
Cited alongside, same era.
The LLUNATIC data-cleaning framework
F. Geerts, G. Mecca, P. Papotti, and D. Santoro · 2013
Cited alongside, same era.
Don’t be scared: use scalable automatic repairing with maximal likelihood and bounded changes
M. Yakout, L. Berti-Équille, and A. K. Elmagarmid · 2013
Cited alongside, same era.
Interaction between record matching and data repairing
W. Fan, S. Ma, N. Tang, and W. Yu · 2014
Cited alongside, same era.
High-dimensional learning of linear causal networks via inverse covariance estimation
P.-L. Loh and P. Bühlmann · 2014
Cited alongside, same era.
Holodetect: Few-shot learning for error detection
A. Heidari, J. McGrath, I. F. Ilyas, and T. Rekatsinas · 2019
Later among the works it cites.
Statistical relational learning based automatic data cleaning
W. Li, L. Li, Z. Li, and M. Cui · 2019
Later among the works it cites.
Raha: A configuration-free error detection system
M. Mahdavi, Z. Abedjan, R. Castro Fernandez, S. Madden, M. Ouzzani, M. Stonebraker, and N. Tang · 2019
Later among the works it cites.
Towards an end-to-end human-centric data cleaning framework
E. K. Rezig, M. Ouzzani, A. K. Elmagarmid, W. G. Aref, and M. Stonebraker · 2019
Later among the works it cites.
A formal framework for probabilistic unclean databases
C. D. Sa, I. F. Ilyas, B. Kimelfeld, C. Ré, and T. Rekatsinas · 2019
Later among the works it cites.
Sparse logistic regression learns all discrete pairwise graphical models
S. Wu, S. Sanghavi, and A. G. Dimakis · 2019
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Towards dependable data repairing with fixing rules
J. Wang and N. Tang · 2014
Cited alongside, same era.
Dimmwitted: A study of main-memory statistical analytics
C. Zhang and C. Ré · 2014
Cited alongside, same era.
pgmpy: Probabilistic graphical models using python
A. Ankan and A. Panda · 2015
Cited alongside, same era.
KATARA: A data cleaning system powered by knowledge bases and crowdsourcing
X. Chu, J. Morcos, I. F. Ilyas, M. Ouzzani, P. Papotti, N. Tang, and Y. Ye · 2015
Cited alongside, same era.
Truth finding on the deep web: Is the problem solved?
X. Li, X. L. Dong, K. Lyons, W. Meng, and D. Srivastava · 2015
Cited alongside, same era.
QASCA: A quality-aware task assignment system for crowdsourcing applications
Y. Zheng, J. Wang, G. Li, R. Cheng, and J. Feng · 2015
Cited alongside, same era.
Later among the works it cites.
Unifying logic rules and machine learning for entity enhancing
W. Fan, P. Lu, and C. Tian · 2020
Later among the works it cites.
Baran: Effective error correction via a unified context representation and transfer learning
M. Mahdavi and Z. Abedjan · 2020
Later among the works it cites.
Batchwise probabilistic incremental data cleaning
P. H. Oliveira, D. S. Kaster, C. T. Jr., and I. F. Ilyas · 2020
Later among the works it cites.
Attention-based learning for missing data imputation in holoclean
R. Wu, A. Zhang, I. F. Ilyas, and T. Rekatsinas · 2020
Later among the works it cites.
A statistical perspective on discovering functional dependencies in noisy data
Y. Zhang, Z. Guo, and T. Rekatsinas · 2020
Later among the works it cites.
Explanations for data repair through shapley values
D. Deutch, N. Frost, A. Gilad, and O. Sheffer · 2021
Later among the works it cites.
Parallel discrepancy detection and incremental detection
W. Fan, C. Tian, Y. Wang, and Q. Yin · 2021
Later among the works it cites.
Pclean: Bayesian data cleaning at scale with domain-specific probabilistic programming
A. K. Lew, M. Agrawal, D. A. Sontag, and V. Mansinghka · 2021
Later among the works it cites.
Cleanml: A study for evaluating the impact of data cleaning on ML classification tasks
P. Li, X. Rao, J. Blase, Y. Zhang, X. Chu, and C. Zhang · 2021
Later among the works it cites.
DAEMA: denoising autoencoder with mask attention
S. Tihon, M. U. Javaid, D. Fourure, N. Posocco, and T. Peel · 2021
Later among the works it cites.
Parallel rule discovery from large datasets by sampling
W. Fan, Z. Han, Y. Wang, and M. Xie · 2022
Later among the works it cites.
Picket: guarding against corrupted data in tabular data during learning and inference
Z. Liu, Z. Zhou, and T. Rekatsinas · 2022
Later among the works it cites.
Self-supervised and interpretable data cleaning with sequence generative adversarial networks
J. Peng, D. Shen, N. Tang, T. Liu, Y. Kou, T. Nie, H. Cui, and G. Yu · 2022
Later among the works it cites.
Using machine learning for agent specifications in agent-based models and simulations: A critical review and guidelines
M. Ale Ebrahim Dehkordi, J. Lechner, A. Ghorbani, I. Nikolic, E. Chappin, and P. Herder · 2023
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CAGAIN: Column attention generative adversarial imputation networks
J. Kawagoshi, Y. Dong, T. Nozawa, and C. Xiao · 2023
Closest in time.