42 citations · 62 across the 7 of their papers we have counts for
11 papers
Distributionally robust tail bounds based on Wasserstein distance and -divergence
Corina Birghila, Maximilian Aigner, Sebastian Engelke
In this work, we provide robust bounds on the tail probabilities and the tail index of heavy-tailed distributions in the context of model misspecification. They are defined as the…
Sparse Structures for Multivariate Extremes
Sebastian Engelke, Jevgenijs Ivanovs
Extreme value statistics provides accurate estimates for the small occurrence probabilities of rare events. While theory and statistical tools for univariate extremes are well-deve…
Causal discovery in heavy-tailed models
Nicola Gnecco, Nicolai Meinshausen, Jonas Peters +1
Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are…
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
Talia B. Kimber, Sebastian Engelke, Igor V. Tetko +2
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Finge…
Graphical Models for Extremes
Sebastian Engelke, Adrien S. Hitz
Conditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theo…
Extreme Value Theory for Open Set Classification -- GPD and GEV Classifiers
Edoardo Vignotto, Sebastian Engelke
Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly enco…