{
  "abstract": "In this paper, we present a lightweight algorithm for modelling tabular data containing both continuous and discrete variables. The method uses empirical marginal distributions and a selected quantile function to transform the data into a continuous latent space, where a distribution of choice, e.g. a multivariate Gaussian, can be efficiently estimated and applied. While this approach is closely related to classical well-established machine learning methods, we present a novel adaptation that fits well within the contemporary computational ecosystem. Unlike in the case of deep architectures, it results in a simple implementation with few dependencies, robust training/estimation, as well as efficient sampling. The transformation preserves complex marginal distributions and correlations, providing high-fidelity models, especially for data with unimodal distributions. One of the key characteristics of the proposed approach is its continuous representation of discrete variables. While highly desirable, this introduces a significant challenge, since a single observation corresponds to a region rather than a point in the latent space. We show that this ambiguity naturally leads to a Monte Carlo expectation-maximisation (MCEM) formulation, whose auxiliary objective can be expressed as a weighted latent-space likelihood. This, in turn, allows conventional maximum-likelihood estimators to be used within an efficient and robust optimisation procedure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21674",
  "author_total": 2,
  "authors": [
    {
      "name": "Bartosz Barzdajn"
    },
    {
      "name": "Mateusz Krzysztoń"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ta8j9vprnna5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21674",
  "fulltext_url": null,
  "id": "7ta8j9vprnna5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21674",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Gaussian Processes and Bayesian Inference"
    ]
  ],
  "openalex": "W7222727002",
  "pdf_url": "https://arxiv.org/pdf/2610.21674",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7ta8j9vprnna5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "2610.21674",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21674"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model",
  "title_text": "Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/7ta8j9vprnna5",
  "venue": "arXiv",
  "versions": []
}