Conditional Density Estimation, Latent Variable Discovery, and Optimal Transport

Hongkang Yang, Esteban G. Tabak

Research output: Contribution to journalArticlepeer-review


A framework is proposed that addresses both conditional density estimation and latent variable discovery. The objective function maximizes explanation of variability in the data, achieved through the optimal transport barycenter generalized to a collection of conditional distributions indexed by a covariate—either given or latent—in any suitable space. Theoretical results establish the existence of barycenters, a minimax formulation of optimal transport maps, and a general characterization of variability via the optimal transport cost. This framework leads to a family of nonparametric neural network-based algorithms, the BaryNet, with a supervised version that estimates conditional distributions and an unsupervised version that assigns latent variables. The efficacy of BaryNets is demonstrated by tests on both artificial and real-world data sets. A parallel drawn between autoencoders and the barycenter framework leads to the Barycentric autoencoder algorithm (BAE).

Original languageEnglish (US)
Pages (from-to)610-663
Number of pages54
JournalCommunications on Pure and Applied Mathematics
Issue number3
StatePublished - Mar 2022

ASJC Scopus subject areas

  • General Mathematics
  • Applied Mathematics


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