---
_id: '14106'
abstract:
- lang: eng
  text: "We show that deep networks trained to satisfy demographic parity often do
    so\r\nthrough a form of race or gender awareness, and that the more we force a
    network\r\nto be fair, the more accurately we can recover race or gender from
    the internal state\r\nof the network. Based on this observation, we investigate
    an alternative fairness\r\napproach: we add a second classification head to the
    network to explicitly predict\r\nthe protected attribute (such as race or gender)
    alongside the original task. After\r\ntraining the two-headed network, we enforce
    demographic parity by merging the\r\ntwo heads, creating a network with the same
    architecture as the original network.\r\nWe establish a close relationship between
    existing approaches and our approach\r\nby showing (1) that the decisions of a
    fair classifier are well-approximated by our\r\napproach, and (2) that an unfair
    and optimally accurate classifier can be recovered\r\nfrom a fair classifier and
    our second head predicting the protected attribute. We use\r\nour explicit formulation
    to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate
    treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios
    under US law."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Michael
  full_name: Lohaus, Michael
  last_name: Lohaus
- first_name: Matthäus
  full_name: Kleindessner, Matthäus
  last_name: Kleindessner
- first_name: Krishnaram
  full_name: Kenthapadi, Krishnaram
  last_name: Kenthapadi
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
- first_name: Chris
  full_name: Russell, Chris
  last_name: Russell
citation:
  ama: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads
    the same as one? Identifying disparate treatment in fair neural networks. In:
    <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. Neural
    Information Processing Systems Foundation; 2022:16548-16562.'
  apa: 'Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., &#38; Russell,
    C. (2022). Are two heads the same as one? Identifying disparate treatment in fair
    neural networks. In <i>36th Conference on Neural Information Processing Systems</i>
    (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information
    Processing Systems Foundation.'
  chicago: Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco
    Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate
    Treatment in Fair Neural Networks.” In <i>36th Conference on Neural Information
    Processing Systems</i>, 35:16548–62. Neural Information Processing Systems Foundation,
    2022.
  ieee: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are
    two heads the same as one? Identifying disparate treatment in fair neural networks,”
    in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans,
    LA, United States, 2022, vol. 35, pp. 16548–16562.
  ista: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are
    two heads the same as one? Identifying disparate treatment in fair neural networks.
    36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information
    Processing Systems, Advances in Neural Information Processing Systems, vol. 35,
    16548–16562.'
  mla: Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate
    Treatment in Fair Neural Networks.” <i>36th Conference on Neural Information Processing
    Systems</i>, vol. 35, Neural Information Processing Systems Foundation, 2022,
    pp. 16548–62.
  short: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:,
    36th Conference on Neural Information Processing Systems, Neural Information Processing
    Systems Foundation, 2022, pp. 16548–16562.
conference:
  end_date: 2022-12-09
  location: New Orleans, LA, United States
  name: 'NeurIPS: Neural Information Processing Systems'
  start_date: 2022-11-28
date_created: 2023-08-21T12:12:42Z
date_published: 2022-12-15T00:00:00Z
date_updated: 2023-09-06T10:29:42Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
  arxiv:
  - '2204.04440'
intvolume: '        35'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2204.04440
month: '12'
oa: 1
oa_version: Preprint
page: 16548-16562
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
  isbn:
  - '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Are two heads the same as one? Identifying disparate treatment in fair neural
  networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '14173'
abstract:
- lang: eng
  text: "Since out-of-distribution generalization is a generally ill-posed problem,
    various proxy targets (e.g., calibration, adversarial robustness, algorithmic
    corruptions, invariance across shifts) were studied across different research
    programs resulting in different recommendations. While sharing the same aspirational
    goal, these approaches have never been tested under the same\r\nexperimental conditions
    on real data. In this paper, we take a unified view of previous work, highlighting
    message discrepancies that we address empirically, and providing recommendations
    on how to measure the robustness of a model and how to improve it. To this end,
    we collect 172 publicly available dataset pairs for training and out-of-distribution
    evaluation of accuracy, calibration error, adversarial attacks, environment invariance,
    and synthetic corruptions. We fine-tune over 31k networks, from nine different
    architectures in the many- and\r\nfew-shot setting. Our findings confirm that
    in- and out-of-distribution accuracies tend to increase jointly, but show that
    their relation is largely dataset-dependent, and in general more nuanced and more
    complex than posited by previous, smaller scale studies."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Florian
  full_name: Wenzel, Florian
  last_name: Wenzel
- first_name: Andrea
  full_name: Dittadi, Andrea
  last_name: Dittadi
- first_name: Peter Vincent
  full_name: Gehler, Peter Vincent
  last_name: Gehler
- first_name: Carl-Johann Simon-Gabriel
  full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
  last_name: Carl-Johann Simon-Gabriel
- first_name: Max
  full_name: Horn, Max
  last_name: Horn
- first_name: Dominik
  full_name: Zietlow, Dominik
  last_name: Zietlow
- first_name: David
  full_name: Kernert, David
  last_name: Kernert
- first_name: Chris
  full_name: Russell, Chris
  last_name: Russell
- first_name: Thomas
  full_name: Brox, Thomas
  last_name: Brox
- first_name: Bernt
  full_name: Schiele, Bernt
  last_name: Schiele
- first_name: Bernhard
  full_name: Schölkopf, Bernhard
  last_name: Schölkopf
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
citation:
  ama: 'Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization
    in transfer learning. In: <i>36th Conference on Neural Information Processing
    Systems</i>. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.'
  apa: 'Wenzel, F., Dittadi, A., Gehler, P. V., Carl-Johann Simon-Gabriel, C.-J. S.-G.,
    Horn, M., Zietlow, D., … Locatello, F. (2022). Assaying out-of-distribution generalization
    in transfer learning. In <i>36th Conference on Neural Information Processing Systems</i>
    (Vol. 35, pp. 7181–7198). New Orleans, LA, United States: Neural Information Processing
    Systems Foundation.'
  chicago: Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel
    Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying
    Out-of-Distribution Generalization in Transfer Learning.” In <i>36th Conference
    on Neural Information Processing Systems</i>, 35:7181–98. Neural Information Processing
    Systems Foundation, 2022.
  ieee: F. Wenzel <i>et al.</i>, “Assaying out-of-distribution generalization in transfer
    learning,” in <i>36th Conference on Neural Information Processing Systems</i>,
    New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198.
  ista: 'Wenzel F, Dittadi A, Gehler PV, Carl-Johann Simon-Gabriel C-JS-G, Horn M,
    Zietlow D, Kernert D, Russell C, Brox T, Schiele B, Schölkopf B, Locatello F.
    2022. Assaying out-of-distribution generalization in transfer learning. 36th Conference
    on Neural Information Processing Systems. NeurIPS: Neural Information Processing
    Systems, Advances in Neural Information Processing Systems, vol. 35, 7181–7198.'
  mla: Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer
    Learning.” <i>36th Conference on Neural Information Processing Systems</i>, vol.
    35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98.
  short: F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel,
    M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf,
    F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural
    Information Processing Systems Foundation, 2022, pp. 7181–7198.
conference:
  end_date: 2022-12-09
  location: New Orleans, LA, United States
  name: 'NeurIPS: Neural Information Processing Systems'
  start_date: 2022-11-28
date_created: 2023-08-22T14:01:13Z
date_published: 2022-12-15T00:00:00Z
date_updated: 2023-09-06T10:34:43Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
  arxiv:
  - '2207.09239'
intvolume: '        35'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2207.09239
month: '12'
oa: 1
oa_version: Preprint
page: 7181-7198
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
  isbn:
  - '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Assaying out-of-distribution generalization in transfer learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '12537'
abstract:
- lang: eng
  text: 'The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide
    memorization, optimization and generalization guarantees in deep neural networks.
    A line of work has studied the NTK spectrum for two-layer and deep networks with
    at least a layer with Ω(N) neurons, N being the number of training samples. Furthermore,
    there is increasing evidence suggesting that deep networks with sub-linear layer
    widths are powerful memorizers and optimizers, as long as the number of parameters
    exceeds the number of samples. Thus, a natural open question is whether the NTK
    is well conditioned in such a challenging sub-linear setup. In this paper, we
    answer this question in the affirmative. Our key technical contribution is a lower
    bound on the smallest NTK eigenvalue for deep networks with the minimum possible
    over-parameterization: the number of parameters is roughly Ω(N) and, hence, the
    number of neurons is as little as Ω(N−−√). To showcase the applicability of our
    NTK bounds, we provide two results concerning memorization capacity and optimization
    guarantees for gradient descent training.'
acknowledgement: "The authors were partially supported by the 2019 Lopez-Loreta prize,
  and they would like to thank\r\nQuynh Nguyen, Mahdi Soltanolkotabi and Adel Javanmard
  for helpful discussions.\r\n"
article_processing_charge: No
arxiv: 1
author:
- first_name: Simone
  full_name: Bombari, Simone
  id: ca726dda-de17-11ea-bc14-f9da834f63aa
  last_name: Bombari
- first_name: Mohammad Hossein
  full_name: Amani, Mohammad Hossein
  last_name: Amani
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
citation:
  ama: 'Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural
    networks with minimum over-parameterization. In: <i>36th Conference on Neural
    Information Processing Systems</i>. Vol 35. Curran Associates; 2022:7628-7640.'
  apa: Bombari, S., Amani, M. H., &#38; Mondelli, M. (2022). Memorization and optimization
    in deep neural networks with minimum over-parameterization. In <i>36th Conference
    on Neural Information Processing Systems</i> (Vol. 35, pp. 7628–7640). Curran
    Associates.
  chicago: Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization
    and Optimization in Deep Neural Networks with Minimum Over-Parameterization.”
    In <i>36th Conference on Neural Information Processing Systems</i>, 35:7628–40.
    Curran Associates, 2022.
  ieee: S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in
    deep neural networks with minimum over-parameterization,” in <i>36th Conference
    on Neural Information Processing Systems</i>, 2022, vol. 35, pp. 7628–7640.
  ista: Bombari S, Amani MH, Mondelli M. 2022. Memorization and optimization in deep
    neural networks with minimum over-parameterization. 36th Conference on Neural
    Information Processing Systems. vol. 35, 7628–7640.
  mla: Bombari, Simone, et al. “Memorization and Optimization in Deep Neural Networks
    with Minimum Over-Parameterization.” <i>36th Conference on Neural Information
    Processing Systems</i>, vol. 35, Curran Associates, 2022, pp. 7628–40.
  short: S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information
    Processing Systems, Curran Associates, 2022, pp. 7628–7640.
date_created: 2023-02-10T13:46:37Z
date_published: 2022-07-24T00:00:00Z
date_updated: 2024-09-10T13:03:19Z
day: '24'
department:
- _id: MaMo
external_id:
  arxiv:
  - '2205.10217'
intvolume: '        35'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: ' https://doi.org/10.48550/arXiv.2205.10217'
month: '07'
oa: 1
oa_version: Preprint
page: 7628-7640
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
  isbn:
  - '9781713871088'
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Memorization and optimization in deep neural networks with minimum over-parameterization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...