{
  "annotation_id": "aa75fe3a-0fe5-4f45-8538-7fc331702fbb",
  "date_created": "2026-02-17T05:53:08.509000Z",
  "date_modified": "2026-02-17T05:53:08.509000Z",
  "file_hash": "8e0b88e44f1a6d96a2937c21c93a0d4cc5da2d7cdace384f6367432223c72ffc",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.06830",
    "authors": [
      "Yinan Hu",
      "Estaban Tabak"
    ],
    "categories": [
      "stat.ML",
      "cs.LG",
      "cs.NA",
      "math.NA",
      "math.OC",
      "math.PR"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Constrained Density Estimation via Optimal Transport",
    "url": "https://arxiv.org/abs/2601.06830",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6c220a39-bc1f-4f4e-8de7-55c6932bed1b",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}