Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments.

Graph-based semi-supervised learning isvery important for manyclassification tasks, but most existing methods assume that all labelled nodes are randomly sampled.

Time series forecasting plays akeyrole in informing industrial and business decisions [17,24,8], while segmentation isuseful forunderstanding biological andphysicalsystems [40,45,34].

More advanced methods dealwith missing data byautoregressivelyreplacing missing observations with predicted ones, eventually using bidirectional architectures [5,6]toexploit both forwardandbackwardtemporal dependencies.

When inserting a new trial, the oldest trial will be removed. We use a population size of 25 and tournament size of 5.

Thus, most meta and transfer-learning HPO methods [7-16] consider a restrictive setting where all tasks must share the same set of hyperparameters so that the input data can be represented as fixed-sizedvectors.