mlsanityData to 14 Oct 2026

Robust Hypothesis Testing Using Wasserstein Uncertainty Sets

arXiv 1805.10611 ↗First posted 27 May 2018stat.ML cs.IT cs.LGcited by 28

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Abstract (arXiv)

We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null and the alternative distributions, which are sets centered around the empirical distribution defined via Wasserstein metric, thus our approach is data-driven and free of distributional assumptions. We develop a convex safe approximation of the minimax formulation and show that such approximation renders a nearly-optimal detector among the family of all possible tests. By exploiting the structure of the least favorable distribution, we also develop a tractable reformulation of such approximation, with complexity independent of the dimension of observation space and can be nearly sample-size-independent in general. Real-data example using human activity data demonstrated the excellent performance of the new robust detector.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 5; 2024: 8; 2025: 4; 2026: 1.

28 papers.

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  1. 2021Distributionally Robust Learningcited by 44
  2. 2023A Finite Sample Complexity Bound for Distributionally Robust Q-learningcited by 36
  3. 2024Credal Learning Theorycited by 31
  4. 2021Statistical Analysis of Wasserstein Distributionally Robust Estimatorscited by 27
  5. 2024Conformalized Credal Regions for Classification with Ambiguous Ground Truthcited by 19
  6. 2021Meta Two-Sample Testing: Learning Kernels for Testing with Limited Datacited by 18
  7. 2025Gen-DFL: Decision-Focused Generative Learning for Robust Decision Makingcited by 18
  8. 2023Outlier-Robust Wasserstein DROcited by 16
  9. 2021Sequential Domain Adaptation by Synthesizing Distributionally Robust Expertscited by 14
  10. 2023Flow-based Distributionally Robust Optimizationcited by 13
  11. 2022A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Setscited by 9
  12. 2023Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimizationcited by 8
16 more
  1. 2024Robust Kernel Hypothesis Testing under Data Corruptioncited by 7
  2. 2024On the Robustness of Kernel Goodness-of-Fit Testscited by 7
  3. 2025Conformal Prediction Regions are Imprecise Highest Density Regionscited by 7
  4. 2022Kernel Robust Hypothesis Testingcited by 6
  5. 2023Uncertainty-Aware Robust Learning on Noisy Graphscited by 6
  6. 2024A Unified Data Representation Learning for Non-parametric Two-sample Testingcited by 6
  7. 2021Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimizationcited by 5
  8. 2024Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Setscited by 4
  9. 2024Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distancescited by 4
  10. 2021Robust Graph Learning Under Wasserstein Uncertaintycited by 3
  11. 2022Adversarial Attack and Defense for Non-Parametric Two-Sample Testscited by 2
  12. 2022Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledgecited by 1
  13. 2025On the Adversarial Robustness of Benjamini Hochbergcited by 1
  14. 2024Optimal Transport for ε-Contaminated Credal Sets: To the Memory of Sayan Mukherjeecited by 0
  15. 2025Hypothesis Testing in Imaging Inverse Problemscited by 0
  16. 2026Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testingcited by 0