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Found 9 papers, 2 papers with code
Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events
We find evidence that machine learning (ML) weather prediction models can locally achieve similar accuracy to HRES on record-shattering events such as the 2021 Pacific Northwest heatwave and even forecast the compound 2021 North American winter storm substantially better.
Extremal graphical modeling with latent variables
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
In particular, we establish a novel connection between the field of distribution generalization from machine learning, and simultaneous equation models and control function from econometrics.
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.
