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.
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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