A Hierarchical Bayesian Approach to Adaptive Multi-Task Modeling - Shaheen Ahmed Jihad Sultan (Indonesian Journal Publisher, 2025)
Multi-task learning (MTL) aims to improve generalization by leveraging shared information across related tasks. However, conventional methods often rely on restrictive, pre-defined assumptions about task relationships, limiting their effectiveness in complex, heterogeneous environments, this paper introduces a Hierarchical Bayesian Model for Adaptive Multi-Task Learning (HB-MTL), a fully integrated probabilistic framework that learns the inter-task relationship structure directly from the data. By placing hyper-priors on the parameters of a shared task distribution, our model can flexibly capture a rich mosaic of relationships, including positive, negative, and null correlations, we employ Variational Inference for tractable posterior approximation, we validate our approach on a challenging synthetic benchmark,