Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data

Authors

• Jiongli Zhu
• Boris Glavic
• Babak Salimi

Materials

bibtex

@inproceedings{HZ25,
  author = {Hu, Kaiyuan and Zhu, Jiongli and Glavic, Boris and Salimi, Babak},
  booktitle = {Proceedings of the International Conference on Management of Data (Demonstration Track)},
  pages = {127--130},
  publisher = {{ACM}},
  year = {2025},
  url = {https://doi.org/10.1145/3722212.3725143},
  doi = {10.1145/3722212.3725143},
  keywords = {Uncertainty; Machine Learning},
  projects = {UA-DB; Zorro},
  video = {https://drive.google.com/file/d/1gxkvRY3pLM0ATco2qvBnM1EcR8-U9F5h/view?usp=sharing},
  pdfurl = {http://www.cs.uic.edu/%7ebglavic/dbgroup/assets/pdfpubls/HZ25.pdf},
  title = {Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data},
  venueshort = {SIGMOD}
}

Reference

Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data Kaiyuan Hu, Jiongli Zhu, Boris Glavic and Babak Salimi Proceedings of the International Conference on Management of Data (Demonstration Track) (2025), pp. 127–130.