22 citations · 45 across the 10 of their papers we have counts for
11 papers
Spatiotemporally Consistent Multivariate Bias Correction for Climate Projections via Nested Vine Copulas
Theresa Meier, Erwan Koch, Valérie Chavez-Demoulin +1
Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observati…
Throwing Vines at the Wall: Structure Learning via Random Search
Thibault Vatter, Thomas Nagler
Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains a key challenge. Early heuristics, suc…
Vine Copulas as Differentiable Computational Graphs
Tuoyuan Cheng, Thibault Vatter, Thomas Nagler +1
Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we int…
Marginalizable Density Models
Dar Gilboa, Ari Pakman, Thibault Vatter
Probability density models based on deep networks have achieved remarkable success in modeling complex high-dimensional datasets. However, unlike kernel density estimators, modern…
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…
Solving estimating equations with copulas
Thomas Nagler, Thibault Vatter
Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. Mor…
Dependent Defaults and Losses with Factor Copula Models
Damien Ackerer, Thibault Vatter
We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models rem…
Generalized Additive Models for Pair-Copula Constructions
Thibault Vatter, Thomas Nagler
Pair-copula constructions are flexible dependence models that use bivariate copulas as building blocks. In this paper, we use generalized additive models to extend them by allowing…