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ICML 2022oral11 citations

Generative Trees: Adversarial and Copycat

Richard Nock, Mathieu Guillame-Bert

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Abstract

While Generative Adversarial Networks (GANs) achieve spectacular results on unstructured data like images, there is still a gap on

BibTeX
@InProceedings{pmlr-v162-nock22a,
  title = 	 {Generative Trees: Adversarial and Copycat},
  author =       {Nock, Richard and Guillame-Bert, Mathieu},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {16906--16951},
  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/nock22a/nock22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/nock22a.html},
  abstract = 	 {While Generative Adversarial Networks (GANs) achieve spectacular results on unstructured data like images, there is still a gap on
Generative Trees: Adversarial and Copycat · ICML 2022