28 citations · 32 across the 8 of their papers we have counts for
10 papers
Spatial Aggregation of ROC and Precision-Recall Curves
Romain Pic, Zhongwei Zhang, Sebastian Engelke +1
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are widely used to assess the discrimination ability of forecasts for binary events, such as threshold exce…
Extrapolation of extreme covariates in generalized additive regression using extreme-value theory
Viviana Carcaiso, Sebastian Engelke, Juliette Legrand +1
Predictions with covariates in the tails of the covariate distribution often lack accuracy and are highly uncertain but may be of critical importance in many applications. New cova…
A Kullback-Leibler divergence test for multivariate extremes: theory and practice
Sebastian Engelke, Philippe Naveau, Chen Zhou
Testing whether two multivariate samples exhibit the same extremal behavior is an important problem in various fields including environmental and climate sciences. While several ad…
Graph structure learning for stable processes
Florian Brück, Sebastian Engelke, Stanislav Volgushev
We introduce Ising-Hüsler-Reiss processes, a new class of multivariate Lévy processes that allows for sparse modeling of the path-wise conditional independence structure between ma…
Intrinsic Whittle--Matérn fields and sparse spatial extremes
David Bolin, Peter Braunsteins, Sebastian Engelke +1
Intrinsic Gaussian fields are used in many areas of statistics as models for spatial or spatio-temporal dependence, or as priors for latent variables. However, there are two major…
Numerical models outperform AI weather forecasts of record-breaking extremes
Zhongwei Zhang, Erich Fischer, Jakob Zscheischler +1
Artificial intelligence (AI)-based models are revolutionizing weather forecasting and have surpassed leading numerical weather prediction systems on various benchmark tasks. Howeve…
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
Olivier C. Pasche, Henry Lam, Sebastian Engelke
Conformal prediction is a popular method to construct prediction intervals with marginal coverage guarantees from black-box machine learning models. In applications with potentiall…
Progression: an extrapolation principle for regression
Gloria Buriticá, Sebastian Engelke
The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such…
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang +2
The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous…
Boosted Control Functions: Distribution generalization and invariance in confounded models
Nicola Gnecco, Jonas Peters, Sebastian Engelke +1
Modern machine learning methods and the availability of large-scale data have significantly advanced our ability to predict target quantities from large sets of covariates. However…