It also makes no assumptions on the sparseness of the transitions. Our experiments reflect this as well,25 as the transition probabilities are drawn from a uniform distribution with no sparseness assumptions and would be26 more difficult tothan sparse cases.

This is useful for a variety of applications like sports analysis (e.g.

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.