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Found 11 papers, 5 papers with code
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
The advantages of this extreme conformal prediction method are illustrated in a simulation study and in an application to flood risk forecasting.
Progression: an extrapolation principle for regression
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.
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
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.
Extremal graphical modeling with latent variables via convex optimization
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
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.
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
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.
Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
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.
Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes
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
Causal questions are omnipresent in many scientific problems.
Methodology
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES.
Extreme Value Theory for Open Set Classification -- GPD and GEV Classifiers
Classification tasks usually assume that all possible classes are present during the training phase.
