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Papers and Books
  • [Invited Paper] Ihab F. Ilyas, Theodoros Rekatsinas, Machine Learning and Data Cleaning: Which Serves the Other?, JDIQ, 2022 [PDF]

  • [Paper] Zifan Liu Zifan, Zhechun Zou, Theodoros Rekatsinas, Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference, VLDB Journal, 2021 [PDF]

  • [Paper] Chang Ge, Shubhankar Mohapatra, Xi He, Ihab F. Ilyas ,Kamino: Constraint-Aware Differentially Private Data Synthesis, PVLDB(14)10, 2021 [PDF]

  • [Paper] Yunjia Zhang, Zhihan Guo, Theodoros Rekatsinas, A Statistical Perspective on Discovering Functional Dependencies in Noisy Data, SIGMOD 2020. [PDF]

  • [Paper] Richard Wu, Aoqian Zhang, Ihab Ilyas, Theodoros Rekatsinas, Attention-based Learning for Missing Data Imputation in HoloClean, MLSys 2020. [PDF]

  • [Book] Ihab F. Ilyas and Xu Chu, Data Cleaning, ACM Books

    [Paper] Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, and Theodoros Rekatsinas, HoloDetect: Few-Shot Learning for Error Detection, SIGMOD, 2019. [PDF]

  • [Paper] Zhihan Guo and Theodoros Rekatsinas, Unsupervised Functional Dependency Discovery for Data Preparation, ICLR, Learning from Limited Data Workshop 2019. [PDF]

  • [Paper] Alireza Heidari, Ihab F. Ilyas, and Theodoros Rekatsinas, Approximate Inference in Structured Instances with Noisy Categorical Observations, UAI 2019. [PDF]

  • [Paper] Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, and Theodoros Rekatsinas, A formal framework for probabilistic unclean databases, ICDT, 2019. [PDF]

  • [Paper] Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, and Christopher Ré, Holoclean: Holistic data repairs with probabilistic inference, PVLDB 10 (2017), no. 11, 1190-1201. [PDF]

  • [Paper] Theodoros Rekatsinas, Manas Joglekar, Hector Garcia-Molina, Aditya Parameswaran, and Christopher Ré, SLiMFast: Guaranteed results for data fusion and source reliability, SIGMOD 2017.[PDF]

  • [Survey] Ihab F. Ilyas and Xu Chu, Trends in Cleaning Relational Data: Cosistency and Deduplications, Foundations and Trends in Databases 2015.[PDF]