---
res:
  bibo_abstract:
  - 'The identification of the parameters of a neural network from finite samples
    of input-output pairs is often referred to as the teacher-student model, and this
    model has represented a popular framework for understanding training and generalization.
    Even if the problem is NP-complete in the worst case, a rapidly growing literature
    – after adding suitable distributional assumptions – has established finite sample
    identification of two-layer networks with a number of neurons (math. formula),
    D being the input dimension. For the range (math. formula) the problem becomes
    harder, and truly little is known for networks parametrized by biases as well.
    This paper fills the gap by providing efficient algorithms and rigorous theoretical
    guarantees of finite sample identification for such wider shallow networks with
    biases. Our approach is based on a two-step pipeline: first, we recover the direction
    of the weights, by exploiting second order information; next, we identify the
    signs by suitable algebraic evaluations, and we recover the biases by empirical
    risk minimization via gradient descent. Numerical results demonstrate the effectiveness
    of our approach.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Massimo
      foaf_name: Fornasier, Massimo
      foaf_surname: Fornasier
  - foaf_Person:
      foaf_givenName: Timo
      foaf_name: Klock, Timo
      foaf_surname: Klock
  - foaf_Person:
      foaf_givenName: Marco
      foaf_name: Mondelli, Marco
      foaf_surname: Mondelli
      foaf_workInfoHomepage: http://www.librecat.org/personId=27EB676C-8706-11E9-9510-7717E6697425
    orcid: 0000-0002-3242-7020
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Rauchensteiner, Michael
      foaf_surname: Rauchensteiner
  bibo_doi: 10.1016/j.acha.2025.101749
  bibo_volume: 77
  dct_date: 2025^xs_gYear
  dct_identifier:
  - UT:001430202700001
  dct_isPartOf:
  - http://id.crossref.org/issn/1063-5203
  - http://id.crossref.org/issn/1096-603X
  dct_language: eng
  dct_publisher: Elsevier@
  dct_title: Efficient identification of wide shallow neural networks with biases@
...