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Found 7 papers, 2 papers with code
Characteristic Neural Ordinary Differential Equations
While NODEs model the evolution of a latent variables as the solution to an ODE, C-NODE models the evolution of the latent variables as the solution of a family of first-order quasi-linear partial differential equations (PDEs) along curves on which the PDEs reduce to ODEs, referred to as characteristic curves.
Generative Archimedean Copulas
We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas.
Modeling Extremes with d-max-decreasing Neural Networks
We propose a novel neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs).
Fisher Auto-Encoders
It has been conjectured that the Fisher divergence is more robust to model uncertainty than the conventional Kullback-Leibler (KL) divergence.
Improved Design of Quadratic Discriminant Analysis Classifier in Unbalanced Settings
The use of quadratic discriminant analysis (QDA) or its regularized version (R-QDA) for classification is often not recommended, due to its well-acknowledged high sensitivity to the estimation noise of the covariance matrix.
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data
This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset.
A Large Dimensional Study of Regularized Discriminant Analysis Classifiers
This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances.
