Improving generalisation via anchor multivariate analysis
arxiv(2024)
摘要
We introduce a causal regularisation extension to anchor regression (AR) for
improved out-of-distribution (OOD) generalisation. We present anchor-compatible
losses, aligning with the anchor framework to ensure robustness against
distribution shifts. Various multivariate analysis (MVA) algorithms, such as
(Orthonormalized) PLS, RRR, and MLR, fall within the anchor framework. We
observe that simple regularisation enhances robustness in OOD settings.
Estimators for selected algorithms are provided, showcasing consistency and
efficacy in synthetic and real-world climate science problems. The empirical
validation highlights the versatility of anchor regularisation, emphasizing its
compatibility with MVA approaches and its role in enhancing replicability while
guarding against distribution shifts. The extended AR framework advances causal
inference methodologies, addressing the need for reliable OOD generalisation.
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