22 citations · 37 across the 3 of their papers we have counts for
4 papers
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
Natasa Tagasovska, Damien Ackerer, Thibault Vatter
We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure. First, an autoencod…
Deep Smoothing of the Implied Volatility Surface
Damien Ackerer, Natasa Tagasovska, Thibault Vatter
We present a neural network (NN) approach to fit and predict implied volatility surfaces (IVSs). Atypically to standard NN applications, financial industry practitioners use such m…
Generative Models for Simulating Mobility Trajectories
Vaibhav Kulkarni, Natasa Tagasovska, Thibault Vatter +1
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications res…
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence…