mlsanityData to 14 Oct 2026

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning

arXiv 2505.23565 ↗First posted 29 May 2025cs.LG cs.MS cs.NAcited by 5

Not on the map. Why?

Abstract (arXiv)

We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

24 of 40 references.

References · 24

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  3. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 337
  4. 2018Learning Models with Uniform Performance via Distributionally Robust Optimizationcited by 335
  5. 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 291
  6. 2016Distributionally Robust Stochastic Optimization with Wasserstein Distancecited by 185
  7. 2016Variance-based regularization with convex objectivescited by 155
  8. 2017Regularization via Mass Transportationcited by 81
  9. 2023Benchmarking Distribution Shift in Tabular Data with TableShiftcited by 66
  10. 2020Distributionally Robust Losses for Latent Covariate Mixturescited by 58
  11. 2021Sinkhorn Distributionally Robust Optimizationcited by 38
  12. 2016Recovering Best Statistical Guarantees via the Empirical Divergence-based Distributionally Robust Optimizationcited by 33
12 more
  1. 2018Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distancescited by 25
  2. 2024Distributionally Robust Optimization and Robust Statisticscited by 24
  3. 2022Holistic Robust Data-Driven Decisionscited by 18
  4. 2021Bayesian Distributionally Robust Optimizationcited by 16
  5. 2023Outlier-Robust Wasserstein DROcited by 16
  6. 2017Data-driven Optimal Cost Selection for Distributionally Robust Optimizationcited by 15
  7. 2022Learning from a Biased Samplecited by 14
  8. 2023Unifying Distributionally Robust Optimization via Optimal Transport Theorycited by 13
  9. 2023Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Datacited by 11
  10. 2023Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimizationcited by 4
  11. 2024textttskwdro: a library for Wasserstein distributionally robust machine learningcited by 4
  12. 2020ROC++: Robust Optimization in C++cited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Worst-case generation via minimax optimization in Wasserstein spacecited by 7
  2. 2026Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contaminationcited by 1
  3. 2025Data Heterogeneity Modeling for Trustworthy Machine Learningcited by 0
  4. 2025Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimizationcited by 0
  5. 2025Distributionally Robust Multimodal Machine Learningcited by 0