Search Results for author: Sebastian Engelke

Found 11 papers, 5 papers with code

Extreme Conformal Prediction: Reliable Intervals for High-Impact Events

no code implementations13 May 2025 Olivier C. Pasche, Henry Lam, Sebastian Engelke

The advantages of this extreme conformal prediction method are illustrated in a simulation study and in an application to flood risk forecasting.

Conformal Prediction Prediction +2

Progression: an extrapolation principle for regression

no code implementations30 Oct 2024 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.

Additive models Out-of-Distribution Generalization +1

Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events

1 code implementation26 Apr 2024 Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang, Jakob Zscheischler, Sebastian Engelke

To address these issues, we compare ML weather prediction models (GraphCast, PanguWeather, and FourCastNet) and ECMWF's high-resolution forecast system (HRES) in three case studies: the 2021 Pacific Northwest heatwave, the 2023 South Asian humid heatwave, and the North American winter storm in 2021.

Deep Learning Prediction +1

Extremal graphical modeling with latent variables via convex optimization

1 code implementation14 Mar 2024 Sebastian Engelke, Armeen Taeb

Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare events.

Boosted Control Functions: Distribution generalization and invariance in confounded models

1 code implementation9 Oct 2023 Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister

This work addresses this gap by introducing a strong notion of invariance that, unlike existing weaker notions, allows for distribution generalization even in the presence of nonlinear, non-identifiable structural functions.

Econometrics

Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk

2 code implementations16 Aug 2022 Olivier C. Pasche, Sebastian Engelke

We propose the EQRN model that combines tools from neural networks and extreme value theory into a method capable of extrapolation in the presence of complex predictor dependence.

quantile regression Time Series +1

Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks

no code implementations30 Oct 2021 Younes Boulaguiem, Jakob Zscheischler, Edoardo Vignotto, Karin van der Wiel, Sebastian Engelke

Here we combine GANs with extreme value theory (evtGAN) to model spatial dependencies in summer maxima of temperature and winter maxima in precipitation over a large part of western Europe.

Management

Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes

no code implementations7 Aug 2020 Michaël Lalancette, Sebastian Engelke, Stanislav Volgushev

Existing work mostly focuses on asymptotic dependence, where the probability of observing a large value in one of the variables is of the same order as observing a large value in all variables simultaneously.

Statistics Theory Methodology Statistics Theory 62G32 (Primary) 62F12, 62G20, 62G30 (Secondary)

Causal discovery in heavy-tailed models

2 code implementations14 Aug 2019 Nicola Gnecco, Nicolai Meinshausen, Jonas Peters, Sebastian Engelke

Causal questions are omnipresent in many scientific problems.

Methodology

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