Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data

Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S. Cheung, Chen-Nee Chuah. Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu 0001, Sivan Sabato, editors, International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Volume 162 of Proceedings of Machine Learning Research, pages 11828-11843, PMLR, 2022. [doi]

@inproceedings{LaiWGCC22,
  title = {Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data},
  author = {Zhengfeng Lai and Chao Wang and Henrry Gunawan and Sen-Ching S. Cheung and Chen-Nee Chuah},
  year = {2022},
  url = {https://proceedings.mlr.press/v162/lai22b.html},
  researchr = {https://researchr.org/publication/LaiWGCC22},
  cites = {0},
  citedby = {0},
  pages = {11828-11843},
  booktitle = {International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA},
  editor = {Kamalika Chaudhuri and Stefanie Jegelka and Le Song and Csaba Szepesvári and Gang Niu 0001 and Sivan Sabato},
  volume = {162},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
}