Web: http://arxiv.org/abs/2206.11646

June 24, 2022, 1:10 a.m. | Mathieu Chevalley, Charlotte Bunne, Andreas Krause, Stefan Bauer

cs.LG updates on arXiv.org arxiv.org

Learning representations that capture the underlying data generating process
is a key problem for data efficient and robust use of neural networks. One key
property for robustness which the learned representation should capture and
which recently received a lot of attention is described by the notion of
invariance. In this work we provide a causal perspective and new algorithm for
learning invariant representations. Empirically we show that this algorithm
works well on a diverse set of tasks and in particular …

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