Analysing Multi-Task Regression via Random Matrix Theory with
Application to Time Series Forecasting
Analysing Multi-Task Regression via Random Matrix Theory with
Application to Time Series Forecasting
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions. We formulate a multi-task optimization problem as a regularization technique to enable single-task models to leverage multi-task learning information. We derive a closed-form solution …