Paper Overview
Field: Machine Learning Authors: Yihong Gu, Katherine Liao, Tianxi Cai Posted: 2026-07-20 arXiv: 2607.18209 Categories: math.ST, cs.LG, stat.ME, stat.ML
Summary
The paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, and auxiliary labels are available in only a subset of these environments. The joint distribution of the covariates may vary across environments, while the latent structure decomposes into:
- Invariant factors with shared loadings across environments
- Heterogeneous factors with environment-specific loadings
- Theoretical guarantee: Using the invariance principle, the authors prove that invariant and heterogeneous factors can be disentangled under minimal structural conditions.
- ATLAS method: ATLAS (Alignment and Transfer of Latent factors across heterogeneous environments with Auxiliary labels and invariance induced Structure) is a unified pipeline that:
- Uses the invariance principle to separate aligned invariant factors from misaligned heterogeneous factors.
- Leverages supervision from auxiliary labels to extract predictive, invariant, and transferable factors even from misaligned heterogeneous factors.
- Performance: ATLAS yields near-oracle performance for downstream latent factor regression and transferable prediction.
Motivated by transfer learning and latent factor regression, the goal is to find a stable low-dimensional representation of a response Y for interpretation and robust out-of-sample prediction.
Contributions
Original Abstract (Condensed)
> We propose ATLAS for disentangling invariant and heterogeneous latent factors across environments using the invariance principle and auxiliary labels, yielding near-oracle performance for downstream latent factor regression and transferable prediction.
Links: arXiv:2607.18209
*Auto-collected on 2026-07-22.*