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Found 3 papers, 1 papers with code
Vector quantile regression and optimal transport, from theory to numerics
In this paper, we first revisit the Koenker and Bassett variational approach to (univariate) quantile regression, emphasizing its link with latent factor representations and correlation maximization problems.
Distribution-Based Invariant Deep Networks for Learning Meta-Features
On both tasks, Dida learns meta-features supporting the characterization of a (labelled) dataset.
Stochastic Deep Networks
This allows to design discriminative networks (to classify or reduce the dimensionality of input measures), generative architectures (to synthesize measures) and recurrent pipelines (to predict measure dynamics).
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