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Found 7 papers, 5 papers with code
Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
Marginalizable Density Models
Probability density models based on deep networks have achieved remarkable success in modeling complex high-dimensional datasets.
Ranked #1 on
Density Estimation
on UCI POWER
Deep Smoothing of the Implied Volatility Surface
Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to value other financial instruments.
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure.
Generative Models for Simulating Mobility Trajectories
We also include two sample tests to assess statistical similarity between the observed and simulated distributions, and we analyze the privacy tradeoffs with respect to membership inference and location-sequence attacks.
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
Generalized Additive Models for Pair-Copula Constructions
Pair-copula constructions are flexible dependence models that use bivariate copulas as building blocks.
Methodology
