In a linear model where main and auxiliary responses share features and the coefficient matrix has low rank, optimally weighting each task's OLS estimate yields a feasible estimator that is asymptotically as efficient as an oracle and dominates OLS.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.ST 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Auxiliary Learning and its Statistical Understanding
In a linear model where main and auxiliary responses share features and the coefficient matrix has low rank, optimally weighting each task's OLS estimate yields a feasible estimator that is asymptotically as efficient as an oracle and dominates OLS.