Generalized Invariant Matching Property via LASSO

• URL: http://arxiv.org/abs/2301.05975v1
• Date: Sat, 14 Jan 2023 21:09:30 GMT
• Title: Generalized Invariant Matching Property via LASSO
• Authors: Kang Du and Yu Xiang
• Abstract summary: In this work, we generalize the invariant matching property by formulating a high-dimensional problem with intrinsic sparsity. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso.
• Score: 19.786769414376323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, we generalize the invariant matching property by formulating a high-dimensional problem with intrinsic sparsity. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.

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