===== Linear unit-tests for invariance discovery =====


//Abstract//:
There is an increasing interest in algorithms to learn invariant correlations across
training environments. A big share of the current proposals find theoretical support in the causality literature but, how useful are they in practice? The purpose
of this note is to propose six linear low-dimensional problems —“unit tests”—
to evaluate different types of out-of-distribution generalization in a precise manner. Following initial experiments, none of the three recently proposed alternatives passes all tests. By providing the code to automatically replicate all
the results in this manuscript (https://www.github.com/facebookresearch/InvarianceUnitTests), we hope that our unit tests become a standard stepping
stone for researchers in out-of-distribution generalization.


<box 99% green>
Benjamin Aubin, Agnieszka Słowik, Martin Arjovsky, Léon Bottou and David Lopez-Paz:  **Linear unit-tests for invariance discovery**,  //arXiv preprint arXiv:2102.10867//, 2021.

[[http://leon.bottou.org/publications/djvu/aubin-2021.djvu|aubin-2021.djvu]]
[[http://leon.bottou.org/publications/pdf/aubin-2021.pdf|aubin-2021.pdf]]
[[http://leon.bottou.org/publications/psgz/aubin-2021.ps.gz|aubin-2021.ps.gz]]
</box>

  @article{aubin-2021,
    title = {Linear unit-tests for invariance discovery},
    author = {Aubin, Benjamin and S{\l}owik, Agnieszka and Arjovsky, Martin and Bottou, L\'{e}on and Lopez-Paz, David},
    journal = {arXiv preprint arXiv:2102.10867},
    year = {2021},
    url = {http://leon.bottou.org/papers/aubin-2021},
  }