Least-squares independence regression for non-linear causal inference under non-Gaussian noise. Yamada, M., Sugiyama, M., & Sese, J. Mach. Learn., 96(3):249–267, 2014.
Least-squares independence regression for non-linear causal inference under non-Gaussian noise [link]Paper  doi  bibtex   
@article{DBLP:journals/ml/YamadaSS14,
  author    = {Makoto Yamada and
               Masashi Sugiyama and
               Jun Sese},
  title     = {Least-squares independence regression for non-linear causal inference
               under non-Gaussian noise},
  journal   = {Mach. Learn.},
  volume    = {96},
  number    = {3},
  pages     = {249--267},
  year      = {2014},
  url       = {https://doi.org/10.1007/s10994-013-5423-y},
  doi       = {10.1007/s10994-013-5423-y},
  timestamp = {Sat, 05 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/ml/YamadaSS14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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