Spectral risk-based learning using unbounded losses

@InProceedings{pmlr-v151-holland22a,
  title = 	 { Spectral risk-based learning using unbounded losses },
  author =       {Holland, Matthew J. and Mehdi Haress, El},
  booktitle = 	 {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {1871--1886},
  year = 	 {2022},
  editor = 	 {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
  volume = 	 {151},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {28--30 Mar},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v151/holland22a/holland22a.pdf},
  url = 	 {https://proceedings.mlr.press/v151/holland22a.html},
  abstract = 	 { In this work, we consider the setting of learning problems under a wide class of spectral risk (or "L-risk") functions, where a Lipschitz-continuous spectral density is used to flexibly assign weight to extreme loss values. We obtain excess risk guarantees for a derivative-free learning procedure under unbounded heavy-tailed loss distributions, and propose a computationally efficient implementation which empirically outperforms traditional risk minimizers in terms of balancing spectral risk and misclassification error. }
}