  This work proposes a novel general-purpose estimator for supervised machine
learning (ML) based on tensor trains (TT). The estimator uses TTs to
parametrize discretized functions, which are then optimized using Riemannian
gradient descent under the form of a tensor completion problem. Since this
optimization is sensitive to initialization, it turns out that the use of other
ML estimators for initialization is crucial. This results in a competitive,
fast ML estimator with lower memory usage than many other ML estimators, like
the ones used for the initialization.
