Making accurate predictions is a crucial factor in many systems. The situation encountered in real-life applications is to have only at disposal incomplete/ rough high-dimensional data, and
extracting predictive model from them is an impossible task unless one relies on some a-priori knowledg of properties of expected model. To
overcome these fundamental challenges, we incorporate additional information through optimization by means of multiparameter
regularization. The main goals of the proposed minisymposium are to set up a new agenda and give a new impulse to the cooperation between
approximation and regularization theories within the intrinsic uncertainty of learning process for real-life data.