SOURCE-LINKED INTELLIGENCE

Multitask Regression with Pairwise Fusion

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

We study multitask regression when coefficient sharing can differ by predictor. For a given predictor, many tasks may have the same coefficient while a few differ, and the exceptional tasks need not be the same for another predictor. We describe this structure by two quantities: the number of active predictors and the total number of task coefficients that differ from the most common value for their predictor. We estimate the coefficient matrix by penalizing all pairwise coefficient differences across tasks, with an additional group penalty when predictor selection is needed. The resulting upp

Read original source ↗ Open in workspace

recordType

paper

region

Global