Diagonalized Attention for Individualized Regression: Latent-Row Localization and Prediction
Modern text and image representations are often matrix-valued, with rows corresponding to tokens, patches, or other local feature vectors. Predictive information is often sparse but sample-specific, making classical sparse regression methods with a common support poorly suited to this heterogeneity. This paper formalizes an individualized sparse regression framework for matrix-valued covariates in which each observation has its own rows of interest, while the associated regression effects are shared across the population. To estimate this model, we introduce a diagonalized attention mechanism that uses query--key scores to localize sample-specific signal rows and a value matrix for downst...
| Authors | Borui Peng, Liwei Lin, Feifei Wang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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