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Found 4 papers, 2 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.
Deep Relaxation: partial differential equations for optimizing deep neural networks
In this paper we establish a connection between non-convex optimization methods for training deep neural networks and nonlinear partial differential equations (PDEs).
Iterative Bregman Projections for Regularized Transportation Problems
This article details a general numerical framework to approximate so-lutions to linear programs related to optimal transport.
Numerical Analysis Analysis of PDEs
Vector Quantile Regression: An Optimal Transport Approach
Under correct specification, the notion produces strong representation, $Y=\beta \left(U\right) ^\top f(Z)$, for $f(Z)$ denoting a known set of transformations of $Z$, where $u \longmapsto \beta(u)^\top f(Z)$ is a monotone map, the gradient of a convex function, and the quantile regression coefficients $u \longmapsto \beta(u)$ have the interpretations analogous to that of the standard scalar quantile regression.
Methodology 49Q20, 49Q10, 90B20
