mlsanityData to 7 Oct 2026

(Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual Algorithm

arXiv 1904.04546 ↗First posted 9 Apr 2019math.OC q-fin.CPcited by 11

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Abstract (arXiv)

In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. As some additional applications, we consider anomaly detection and automatic generation of financial data.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2019.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 4; 2024: 1; 2025: 1.

11 papers.

  1. 2022Entropic Neural Optimal Transport via Diffusion Processescited by 49
  2. 2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transportcited by 39
  3. 2022Neural Optimal Transport with General Cost Functionalscited by 34
  4. 2022Kernel Neural Optimal Transportcited by 31
  5. 2023Light Schr"odinger Bridgecited by 18
  6. 2024Estimating Barycenters of Distributions with Neural Optimal Transportcited by 14
  7. 2023Extremal Domain Translation with Neural Optimal Transportcited by 12
  8. 2023Energy-Guided Continuous Entropic Barycenter Estimation for General Costscited by 12
  9. 2021A deep learning approach to data-driven model-free pricing and to martingale optimal transportcited by 6
  10. 2022Detecting data-driven robust statistical arbitrage strategies with deep neural networkscited by 4
  11. 2025Expert or not? assessing data quality in offline reinforcement learningcited by 1