Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model

Key points

• 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.1
• 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.1
• One of the key characteristics of the proposed approach is its continuous representation of discrete variables.1
• 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.1

Sources (1)

• [1]Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 10:58 AM

  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.

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