Invariant Causal Mechanisms through Distribution Matching

• URL: http://arxiv.org/abs/2206.11646v1
• Date: Thu, 23 Jun 2022 12:06:54 GMT
• Title: Invariant Causal Mechanisms through Distribution Matching
• Authors: Mathieu Chevalley, Charlotte Bunne, Andreas Krause, Stefan Bauer
• Abstract summary: In this work we provide a causal perspective and a new algorithm for learning invariant representations. Empirically we show that this algorithm works well on a diverse set of tasks and in particular we observe state-of-the-art performance on domain generalization.
• Score: 86.07327840293894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 we observe state-of-the-art performance on domain generalization, where we are able to significantly boost the score of existing models.

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