Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
The study investigates how task diversity is distributed within linear meta-learning models. It found that performance drops when a large portion of task variability is in 'orthogonal, non-informative directions' relative to the shared structure. This provides a principled way to design better meta-learning algorithms by understanding how structural diversity impacts prediction accuracy.