---
OA_place: publisher
OA_type: gold
_id: '18897'
abstract:
- lang: eng
  text: 'Score-based generative models (SGMs) are powerful tools to sample from complex
    data distributions. Their underlying idea is to (i) run a forward process for
    time T1 by adding noise to the data, (ii) estimate its score function, and (iii)
    use such estimate to run a reverse process. As the reverse process is initialized
    with the stationary distribution of the forward one, the existing analysis paradigm
    requires T1→∞. This is however problematic: from a theoretical viewpoint, for
    a given precision of the score approximation, the convergence guarantee fails
    as T1 diverges; from a practical viewpoint, a large T1 increases computational
    costs and leads to error propagation. This paper addresses the issue by considering
    a version of the popular predictor-corrector scheme: after running the forward
    process, we first estimate the final distribution via an inexact Langevin dynamics
    and then revert the process. Our key technical contribution is to provide convergence
    guarantees which require to run the forward process only for a fixed finite time
    T1. Our bounds exhibit a mild logarithmic dependence on the input dimension and
    the subgaussian norm of the target distribution, have minimal assumptions on the
    data, and require only to control the L2 loss on the score approximation, which
    is the quantity minimized in practice.'
acknowledgement: "Francesco Pedrotti and Jan Maas acknowledge support by the Austrian
  Science Fund (FWF) project 10.55776/F65. Marco Mondelli acknowledges support by
  the 2019 Lopez-Loreta prize.\r\n"
alternative_title:
- TMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
  full_name: Pedrotti, Francesco
  id: d3ac8ac6-dc8d-11ea-abe3-e2a9628c4c3c
  last_name: Pedrotti
- first_name: Jan
  full_name: Maas, Jan
  id: 4C5696CE-F248-11E8-B48F-1D18A9856A87
  last_name: Maas
  orcid: 0000-0002-0845-1338
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
citation:
  ama: 'Pedrotti F, Maas J, Mondelli M. Improved convergence of score-based diffusion
    models via prediction-correction. In: <i>Transactions on Machine Learning Research</i>.
    ; 2024.'
  apa: Pedrotti, F., Maas, J., &#38; Mondelli, M. (2024). Improved convergence of
    score-based diffusion models via prediction-correction. In <i>Transactions on
    Machine Learning Research</i>.
  chicago: Pedrotti, Francesco, Jan Maas, and Marco Mondelli. “Improved Convergence
    of Score-Based Diffusion Models via Prediction-Correction.” In <i>Transactions
    on Machine Learning Research</i>, 2024.
  ieee: F. Pedrotti, J. Maas, and M. Mondelli, “Improved convergence of score-based
    diffusion models via prediction-correction,” in <i>Transactions on Machine Learning
    Research</i>, 2024.
  ista: Pedrotti F, Maas J, Mondelli M. 2024. Improved convergence of score-based
    diffusion models via prediction-correction. Transactions on Machine Learning Research.
    , TMLR, .
  mla: Pedrotti, Francesco, et al. “Improved Convergence of Score-Based Diffusion
    Models via Prediction-Correction.” <i>Transactions on Machine Learning Research</i>,
    2024.
  short: F. Pedrotti, J. Maas, M. Mondelli, in:, Transactions on Machine Learning
    Research, 2024.
corr_author: '1'
date_created: 2025-01-27T12:18:05Z
date_published: 2024-06-01T00:00:00Z
date_updated: 2025-04-15T08:31:35Z
day: '01'
ddc:
- '000'
department:
- _id: JaMa
- _id: MaMo
external_id:
  arxiv:
  - '2305.14164'
file:
- access_level: open_access
  checksum: 76a1fd5afd8ee6f7ae0e5912d7dbf6b4
  content_type: application/pdf
  creator: dernst
  date_created: 2025-01-27T12:19:44Z
  date_updated: 2025-01-27T12:19:44Z
  file_id: '18898'
  file_name: 2024_TMLR_Pedrotti.pdf
  file_size: 780315
  relation: main_file
  success: 1
file_date_updated: 2025-01-27T12:19:44Z
has_accepted_license: '1'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2
  grant_number: F6504
  name: Taming Complexity in Partial Differential Systems
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Transactions on Machine Learning Research
publication_identifier:
  issn:
  - 2835-8856
publication_status: published
quality_controlled: '1'
related_material:
  record:
  - id: '17350'
    relation: earlier_version
    status: public
scopus_import: '1'
status: public
title: Improved convergence of score-based diffusion models via prediction-correction
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '12660'
abstract:
- lang: eng
  text: 'We present Cross-Client Label Propagation(XCLP), a new method for transductive
    federated learning. XCLP estimates a data graph jointly from the data of multiple
    clients and computes labels for the unlabeled data by propagating label information
    across the graph. To avoid clients having to share their data with anyone, XCLP
    employs two cryptographically secure protocols: secure Hamming distance computation
    and secure summation. We demonstrate two distinct applications of XCLP within
    federated learning. In the first, we use it in a one-shot way to predict labels
    for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled
    training data in a federated semi-supervised setting. Experiments on both real
    federated and standard benchmark datasets show that in both applications XCLP
    achieves higher classification accuracy than alternative approaches.'
alternative_title:
- TMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Jonathan A
  full_name: Scott, Jonathan A
  id: e499926b-f6e0-11ea-865d-9c63db0031e8
  last_name: Scott
- first_name: Michelle X
  full_name: Yeo, Michelle X
  id: 2D82B818-F248-11E8-B48F-1D18A9856A87
  last_name: Yeo
  orcid: 0009-0001-3676-4809
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Scott JA, Yeo MX, Lampert C. Cross-client label propagation for transductive
    and semi-supervised federated learning. In: <i>Transactions in Machine Learning</i>.
    Curran Associates; 2023.'
  apa: Scott, J. A., Yeo, M. X., &#38; Lampert, C. (2023). Cross-client label propagation
    for transductive and semi-supervised federated learning. In <i>Transactions in
    Machine Learning</i>. Curran Associates.
  chicago: Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client
    Label Propagation for Transductive and Semi-Supervised Federated Learning.” In
    <i>Transactions in Machine Learning</i>. Curran Associates, 2023.
  ieee: J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client label propagation for
    transductive and semi-supervised federated learning,” in <i>Transactions in Machine
    Learning</i>, 2023.
  ista: Scott JA, Yeo MX, Lampert C. 2023. Cross-client label propagation for transductive
    and semi-supervised federated learning. Transactions in Machine Learning. , TMLR,
    .
  mla: Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive
    and Semi-Supervised Federated Learning.” <i>Transactions in Machine Learning</i>,
    Curran Associates, 2023.
  short: J.A. Scott, M.X. Yeo, C. Lampert, in:, Transactions in Machine Learning,
    Curran Associates, 2023.
corr_author: '1'
date_created: 2023-02-20T08:21:50Z
date_published: 2023-11-27T00:00:00Z
date_updated: 2025-02-04T08:32:19Z
day: '27'
ddc:
- '004'
department:
- _id: ChLa
external_id:
  arxiv:
  - '2210.06434'
file:
- access_level: open_access
  checksum: aa322ad91cbd229f5cafe6733a119bd1
  content_type: application/pdf
  creator: dernst
  date_created: 2025-02-04T08:30:05Z
  date_updated: 2025-02-04T08:30:05Z
  file_id: '18990'
  file_name: 2023_TMLR_Scott.pdf
  file_size: 553717
  relation: main_file
  success: 1
file_date_updated: 2025-02-04T08:30:05Z
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Preprint
publication: Transactions in Machine Learning
publication_identifier:
  issn:
  - 2835-8856
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/jonnyascott/xclp
status: public
title: Cross-client label propagation for transductive and semi-supervised federated
  learning
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12495'
abstract:
- lang: eng
  text: "Fairness-aware learning aims at constructing classifiers that not only make
    accurate predictions, but also do not discriminate against specific groups. It
    is a fast-growing area of\r\nmachine learning with far-reaching societal impact.
    However, existing fair learning methods\r\nare vulnerable to accidental or malicious
    artifacts in the training data, which can cause\r\nthem to unknowingly produce
    unfair classifiers. In this work we address the problem of\r\nfair learning from
    unreliable training data in the robust multisource setting, where the\r\navailable
    training data comes from multiple sources, a fraction of which might not be representative
    of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat
    identifies and suppresses those data sources that would have a negative impact
    on\r\nfairness or accuracy if they were used for training. As such, FLEA is not
    a replacement of\r\nprior fairness-aware learning methods but rather an augmentation
    that makes any of them\r\nrobust against unreliable training data. We show the
    effectiveness of our approach by a\r\ndiverse range of experiments on multiple
    datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects
    the learner against corruptions as long as the fraction of\r\naffected data sources
    is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: 'The authors would like to thank Bernd Prach, Elias Frantar, Alexandra
  Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research
  was supported by the Scientific Service Units (SSU) of IST Austria through resources
  provided by Scientific Computing (SciComp). This publication was made possible by
  an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia
  Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. '
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Eugenia B
  full_name: Iofinova, Eugenia B
  id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
  last_name: Iofinova
  orcid: 0000-0002-7778-3221
- first_name: Nikola H
  full_name: Konstantinov, Nikola H
  id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
  last_name: Konstantinov
  orcid: 0009-0009-5204-7621
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource
    learning from unreliable training data. <i>Transactions on Machine Learning Research</i>.
    2022.'
  apa: 'Iofinova, E. B., Konstantinov, N. H., &#38; Lampert, C. (2022). FLEA: Provably
    robust fair multisource learning from unreliable training data. <i>Transactions
    on Machine Learning Research</i>. ML Research Press.'
  chicago: 'Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA:
    Provably Robust Fair Multisource Learning from Unreliable Training Data.” <i>Transactions
    on Machine Learning Research</i>. ML Research Press, 2022.'
  ieee: 'E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust
    fair multisource learning from unreliable training data,” <i>Transactions on Machine
    Learning Research</i>. ML Research Press, 2022.'
  ista: 'Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair
    multisource learning from unreliable training data. Transactions on Machine Learning
    Research.'
  mla: 'Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning
    from Unreliable Training Data.” <i>Transactions on Machine Learning Research</i>,
    ML Research Press, 2022.'
  short: E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning
    Research (2022).
corr_author: '1'
date_created: 2023-02-02T20:29:57Z
date_published: 2022-12-22T00:00:00Z
date_updated: 2025-12-30T11:04:31Z
day: '22'
ddc:
- '000'
department:
- _id: ChLa
external_id:
  arxiv:
  - '2106.11732'
file:
- access_level: open_access
  checksum: 97c8a8470759cab597abb973ca137a3b
  content_type: application/pdf
  creator: dernst
  date_created: 2023-02-23T10:30:04Z
  date_updated: 2023-02-23T10:30:04Z
  file_id: '12673'
  file_name: 2022_TMLR_Iofinova.pdf
  file_size: 1948063
  relation: main_file
  success: 1
file_date_updated: 2023-02-23T10:30:04Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://openreview.net/forum?id=XsPopigZXV
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
  grant_number: W1260-N35
  name: Vienna Graduate School on Computational Optimization
publication: Transactions on Machine Learning Research
publication_identifier:
  issn:
  - 2835-8856
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
  link:
  - description: source code
    relation: software
    url: https://github.com/ISTAustria-CVML/FLEA
status: public
title: 'FLEA: Provably robust fair multisource learning from unreliable training data'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...