@inproceedings{18897,
  abstract     = {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.},
  author       = {Pedrotti, Francesco and Maas, Jan and Mondelli, Marco},
  booktitle    = {Transactions on Machine Learning Research},
  issn         = {2835-8856},
  title        = {{Improved convergence of score-based diffusion models via prediction-correction}},
  year         = {2024},
}

@inproceedings{12660,
  abstract     = {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.},
  author       = {Scott, Jonathan A and Yeo, Michelle X and Lampert, Christoph},
  booktitle    = {Transactions in Machine Learning},
  issn         = {2835-8856},
  publisher    = {Curran Associates},
  title        = {{Cross-client label propagation for transductive and semi-supervised federated learning}},
  year         = {2023},
}

@article{12495,
  abstract     = {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
machine learning with far-reaching societal impact. However, existing fair learning methods
are vulnerable to accidental or malicious artifacts in the training data, which can cause
them to unknowingly produce unfair classifiers. In this work we address the problem of
fair learning from unreliable training data in the robust multisource setting, where the
available 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
that identifies and suppresses those data sources that would have a negative impact on
fairness or accuracy if they were used for training. As such, FLEA is not a replacement of
prior fairness-aware learning methods but rather an augmentation that makes any of them
robust against unreliable training data. We show the effectiveness of our approach by a
diverse range of experiments on multiple datasets. Additionally, we prove formally that
–given enough data– FLEA protects the learner against corruptions as long as the fraction of
affected data sources is less than half. Our source code and documentation are available at
https://github.com/ISTAustria-CVML/FLEA.},
  author       = {Iofinova, Eugenia B and Konstantinov, Nikola H and Lampert, Christoph},
  issn         = {2835-8856},
  journal      = {Transactions on Machine Learning Research},
  publisher    = {ML Research Press},
  title        = {{FLEA: Provably robust fair multisource learning from unreliable training data}},
  year         = {2022},
}