Learning curves of generic features maps for realistic datasets with a teacher-student model

Understand

Teacher-student models provide a framework in which the typical-case performance of high-dimensional supervised learning can be described in closed form.

• The assumptions of Gaussian i.i.d.
• input data underlying the canonical teacher-student model may, however, be perceived as too restrictive to capture the behaviour of realistic data sets.
• In this paper, we introduce a Gaussian covariate generalisation of the model where the teacher and student can act on different spaces, generated with fixed, but generic feature maps.