IDEA

Raw data is usually dirty , e.g., missing or inconsistent information, and needs substantial amounts of resources to clean. Data cleaning is a major obstacle in ML and inference on large data. In this project, we propose efficient methods to learn accurate models or infer accurate results over dirty data without cleaning.

Publications

• Certain and Approximately Certain Models for Statistical Learning 
  Cheng Zhen, Nischal Arya, Arash Termehchy, and Amandeep Singh Chabada
  The Proceedings of the ACM on Management of Data (SIGMOD), Article 126, 2024
• When Can We Ignore Missing Data in Model Training?    [Slides][Code+Data] 
  Cheng Zhen, Amandeep Singh Chabada, Arash Termehchy
  In Proceedings of SIGMOD Workshop on Data Management for End-to-End Machine Learning (DEEM), June 2023.
• Learning Over Dirty Data Without Cleaning    [Slides][Code+Data] 
  Jose Picado, John Davis, Arash Termehchy, and Claire Lee
  The Proceedings of SIGMOD, 2020.
  Technical report with proofs
• Learning Efficiently Over Heterogenous Databases    [Poster]
  Jose Picado, Sudhanshu Pathak, and Arash Termehchy
  The Proceedings of the VLDB Endowment (Demonstration Track) , August 2018.

People

• Arash Termehchy
• Cheng Zhen
• Nischal Aryal
• Jose Picado
• Amandeep Singh Chabada
• John Davis
• Claire Lee