  Hyperparameter selection in continual learning scenarios is a challenging and
underexplored aspect, especially in practical non-stationary environments.
Traditional approaches, such as grid searches with held-out validation data
from all tasks, are unrealistic for building accurate lifelong learning
systems. This paper aims to explore the role of hyperparameter selection in
continual learning and the necessity of continually and automatically tuning
them according to the complexity of the task at hand. Hence, we propose
leveraging the nature of sequence task learning to improve Hyperparameter
Optimization efficiency. By using the functional analysis of variance-based
techniques, we identify the most crucial hyperparameters that have an impact on
performance. We demonstrate empirically that this approach, agnostic to
continual scenarios and strategies, allows us to speed up hyperparameters
optimization continually across tasks and exhibit robustness even in the face
of varying sequential task orders. We believe that our findings can contribute
to the advancement of continual learning methodologies towards more efficient,
robust and adaptable models for real-world applications.
