Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds

Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe 0001. Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds. In Vitaly Feldman, Katrina Ligett, Sivan Sabato, editors, Algorithmic Learning Theory, 16-19 March 2021, Virtual Conference, Worldwide. Volume 132 of Proceedings of Machine Learning Research, pages 599-618, PMLR, 2021. [doi]

@inproceedings{Emamjomeh-Zadeh21,
  title = {Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds},
  author = {Ehsan Emamjomeh-Zadeh and Chen-Yu Wei and Haipeng Luo and David Kempe 0001},
  year = {2021},
  url = {http://proceedings.mlr.press/v132/emamjomeh-zadeh21a.html},
  researchr = {https://researchr.org/publication/Emamjomeh-Zadeh21},
  cites = {0},
  citedby = {0},
  pages = {599-618},
  booktitle = {Algorithmic Learning Theory, 16-19 March 2021, Virtual Conference, Worldwide},
  editor = {Vitaly Feldman and Katrina Ligett and Sivan Sabato},
  volume = {132},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
}