Multi-task learning via robust regularized clustering with non-convex group penalties

arXiv:2404.03250v1 Announce Type: cross
Abstract: Multi-task learning (MTL) aims to improve estimation and prediction performance by sharing common information among related tasks. One natural assumption in MTL is that tasks are classified into clusters based on their characteristics. However, existing MTL methods based on this assumption often ignore outlier tasks that have large task-specific components or no relation to other tasks. To address this issue, we propose a novel MTL method called Multi-Task Learning via Robust Regularized Clustering (MTLRRC). MTLRRC …

abstract arxiv clustering cs.lg however information multi-task learning natural outlier performance prediction robust stat.me stat.ml tasks type via