资源论文On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning

On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning

2020-02-26 | |  89 |   47 |   0

Abstract

A learning problem might have several measures of complexity (e.g., norm and dimensionality) that affect the generalization error. What is the interaction between these complexities? Dimension-free learning theory bounds and parametric asymptotic analyses each provide a partial picture of the full learning curve. In this paper, we use high-dimensional asymptotics on two classical problems—mean estimation and linear regression—to explore the learning curve more completely. We show that these curves exhibit multiple regimes, where in each regime, the excess risk is controlled by a subset of the problem complexities.

上一篇:A DC Programming Approach for Sparse Eigenvalue Problem

下一篇:Supervised Aggregation of Classifiers using Artificial Prediction Markets

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...