Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms

@InProceedings{pmlr-v89-blondel19a,
  title = 	 {Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms},
  author =       {Blondel, Mathieu and Martins, Andre and Niculae, Vlad},
  booktitle = 	 {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
  pages = 	 {606--615},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Sugiyama, Masashi},
  volume = 	 {89},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {16--18 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v89/blondel19a/blondel19a.pdf},
  url = 	 {https://proceedings.mlr.press/v89/blondel19a.html},
  abstract = 	 {This paper studies Fenchel-Young losses, a generic way to construct convex loss functions from a regularization function.  We analyze their properties in depth, showing that they unify many well-known loss functions and allow to create useful new ones easily.  Fenchel-Young losses constructed from a generalized entropy, including the Shannon and Tsallis entropies, induce predictive probability distributions.  We formulate conditions for a generalized entropy to yield losses with a separation margin, and probability distributions with sparse support.  Finally, we derive efficient algorithms, making Fenchel-Young losses appealing both in theory and practice.}
}