Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments. The joint distribution of the covariates may vary across environments, whereas the latent structure is decomposed into invariant factors with shared loadings and heterogeneous factors with environment-specific loadings. Such a model is motivated by transfer learning and latent factor regression, where one seeks stable low-dimensional representations for both interpretation and robust out-of-sample prediction of the response $Y$. Leveraging the invariance principle, we show that the invar...
| Authors | Yihong Gu, Katherine Liao, Tianxi Cai |
|---|---|
| Primary category | math.ST |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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