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

May 21, 2021 (edited Jan 21, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Statistical Physics, Teacher-Student, Replica method, High-dimensional statistics, Gordon comparison theorem, Kernel Methods
  • TL;DR: We provide a rigorous solution to an extension of the teacher-student problem in high-dimension, and apply it to real structured datasets.
  • Abstract: 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. While still solvable in a closed form, this generalization is able to capture the learning curves for a broad range of realistic data sets, thus redeeming the potential of the teacher-student framework. Our contribution is then two-fold: first, we prove a rigorous formula for the asymptotic training loss and generalisation error. Second, we present a number of situations where the learning curve of the model captures the one of a realistic data set learned with kernel regression and classification, with out-of-the-box feature maps such as random projections or scattering transforms, or with pre-learned ones - such as the features learned by training multi-layer neural networks. We discuss both the power and the limitations of the framework.
  • Supplementary Material: pdf
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  • Code: https://github.com/IdePHICS/GCMProject
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