Learning from Uncertain Data: From Possible Worlds to Possible Models

Authors

• Jiongli Zhu
• Su Feng
• Boris Glavic
• Babak Salimi

Materials

bibtex

@inproceedings{ZF24,
  author = {Zhu, Jiongli and Feng, Su and Glavic, Boris and Salimi, Babak},
  title = {Learning from Uncertain Data: From Possible Worlds to Possible Models},
  pdfurl = {http://www.cs.uic.edu/%7ebglavic/dbgroup/assets/pdfpubls/ZF24.pdf},
  longversionurl = {https://arxiv.org/pdf/2405.18549},
  booktitle = {Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024},
  year = {2024},
  url = {http://papers.nips.cc/paper\_files/paper/2024/hash/c17fab1bcef325d0d30989c9bf506c0b-Abstract-Conference.html},
  editor = {Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M. and Zhang, Cheng},
  projects = {UA-DB; Zorro},
  venueshort = {{NeurIPS}},
  keywords = {Uncertainty; Machine Learning}
}

Reference

Learning from Uncertain Data: From Possible Worlds to Possible Models Jiongli Zhu, Su Feng, Boris Glavic and Babak Salimi Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 (2024).