  Multi-task learning (MTL) is frequently used in settings where a target task
has to be learnt based on limited training data, but knowledge can be leveraged
from related auxiliary tasks. While MTL can improve task performance overall
relative to single-task learning (STL), these improvements can hide negative
transfer (NT), where STL may deliver better performance for many individual
tasks. Asymmetric multitask feature learning (AMTFL) is an approach that tries
to address this by allowing tasks with higher loss values to have smaller
influence on feature representations for learning other tasks. Task loss values
do not necessarily indicate reliability of models for a specific task. We
present examples of NT in two orthogonal datasets (image recognition and
pharmacogenomics) and tackle this challenge by using aleatoric homoscedastic
uncertainty to capture the relative confidence between tasks, and set weights
for task loss. Our results show that this approach reduces NT providing a new
approach to enable robust MTL.
