mlsanityData to 14 Oct 2026

textttskwdro: a library for Wasserstein distributionally robust machine learning

arXiv 2410.21231 ↗First posted 28 Oct 2024cs.LG cs.MS math.OCcited by 4

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Abstract (arXiv)

We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using Wasserstein distances, popular in optimal transport and machine learnings. The goal of the library is to make the training of robust models easier for a wide audience by proposing a wrapper for PyTorch modules, enabling model loss' robustification with minimal code changes. It comes along with scikit-learn compatible estimators for some popular objectives. The core of the implementation relies on an entropic smoothing of the original robust objective, in order to ensure maximal model flexibility. The library is available at https://github.com/iutzeler/skwdro and the documentation at https://skwdro.readthedocs.io.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

14 of 21 references.

References · 14

  1. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  2. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  3. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 337
  4. 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 291
  5. 2019Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learningcited by 170
  6. 2017Regularization via Mass Transportationcited by 81
  7. 2021Distributionally Robust Learningcited by 44
  8. 2021Sinkhorn Distributionally Robust Optimizationcited by 38
  9. 2021Statistical Analysis of Wasserstein Distributionally Robust Estimatorscited by 27
  10. 2024Distributionally Robust Optimization and Robust Statisticscited by 24
  11. 2023Mechanic: A Learning Rate Tunercited by 20
  12. 2023Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Modelscited by 9
2 more
  1. 2023Distributionally Robust Optimization with Bias and Variance Reductioncited by 6
  2. 2024Universal generalization guarantees for Wasserstein distributionally robust modelscited by 5

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025DRO: A Python Library for Distributionally Robust Optimization in Machine Learningcited by 5
  2. 2025A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimizationcited by 1
  3. 2025Iterative Sampling Methods for Sinkhorn Distributionally Robust Optimizationcited by 1
  4. 2025Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimizationcited by 0