Diagonalized Attention for Individualized Regression: Latent-Row Localization and Prediction
Read the original on arXiv Machine Learning →The paper introduces an individualized sparse regression framework for matrix‑valued covariates, where each observation has its own relevant rows while regression effects are shared across the population. It proposes a diagonalized attention mechanism that uses query–key scores to localize sample‑specific signal rows and a value matrix for downstream regression, achieving a parameter dimension independent of sample size. The authors provide existence theorems guaranteeing recovery of latent rows under score‑separation and concentration conditions, and demonstrate strong prediction, localization, and classification performance in simulations and real sentiment analysis.
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