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Empirische Inferenz Conference Paper 2021

Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation

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Empirische Inferenz
Jia-Jie Zhu
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Empirische Inferenz
Wittawat Jitkrittum
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Empirische Inferenz
Bernhard Schölkopf
  • Director

Author(s): Zhu, J.-J. and Jitkrittum, W. and Diehl, M. and Schölkopf, B.
Book Title: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS)
Volume: 130
Pages: 280--288
Year: 2021
Month: April
Series: Proceedings of Machine Learning Research
Editors: Arindam Banerjee and Kenji Fukumizu
Publisher: PMLR
Project(s):
Bibtex Type: Conference Paper (conference)
Event Place: Virtual Conference
State: Published
URL: http://proceedings.mlr.press/v130/zhu21a.html
Electronic Archiving: grant_archive
Links:

BibTex 

@conference{zhu20kdro,
  title = {Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation},
  booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS)},
  volume = {130},
  pages = {280--288},
  series = {Proceedings of Machine Learning Research},
  editors = {Arindam Banerjee and Kenji Fukumizu},
  publisher = {PMLR},
  month = apr,
  year = {2021},
  slug = {zhu20kdro},
  author = {Zhu, J.-J. and Jitkrittum, W. and Diehl, M. and Sch{\"o}lkopf, B.},
  url = {http://proceedings.mlr.press/v130/zhu21a.html},
  month_numeric = {4}
}