Constrained Density Estimation via Optimal Transport
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915810667134976 |
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| author | Hu, Yinan Tabak, Esteban G. |
| author_facet | Hu, Yinan Tabak, Esteban G. |
| contents | A novel framework for density estimation under expectation constraints is proposed. The framework minimizes the Wasserstein distance between the estimated density and a prior, subject to the constraints that the expected value of a set of functions adopts or exceeds given values. The framework is generalized to include regularization inequalities to mitigate the artifacts in the target measure. An annealing-like algorithm is developed to address non-smooth constraints, with its effectiveness demonstrated through both synthetic and proof-of-concept real world examples in finance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06830 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Constrained Density Estimation via Optimal Transport Hu, Yinan Tabak, Esteban G. Machine Learning Numerical Analysis Optimization and Control Probability A novel framework for density estimation under expectation constraints is proposed. The framework minimizes the Wasserstein distance between the estimated density and a prior, subject to the constraints that the expected value of a set of functions adopts or exceeds given values. The framework is generalized to include regularization inequalities to mitigate the artifacts in the target measure. An annealing-like algorithm is developed to address non-smooth constraints, with its effectiveness demonstrated through both synthetic and proof-of-concept real world examples in finance. |
| title | Constrained Density Estimation via Optimal Transport |
| topic | Machine Learning Numerical Analysis Optimization and Control Probability |
| url | https://arxiv.org/abs/2601.06830 |