Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more

@InProceedings{pmlr-v162-tolochinksy22a,
  title = 	 {Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more},
  author =       {Tolochinksy, Elad and Jubran, Ibrahim and Feldman, Dan},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {21520--21547},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/tolochinksy22a/tolochinksy22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/tolochinksy22a.html},
  abstract = 	 {Coreset (or core-set) is a small weighted