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Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system., , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (December 2023)Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression., , , and . IEEE Trans. Smart Grid, 7 (5): 2448-2455 (2016)INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles, , , and . Extremes, 21 (3): 441--462 (Sep 1, 2018)A unifying partially-interpretable framework for neural network-based extreme quantile regression., and . CoRR, (2022)Advances in statistical modeling of spatial extremes, and . WIREs Computational Statistics, n/a (n/a): e1537 (2020)Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model., , , , and . Environ. Model. Softw., (2017)Hierarchical Archimax copulas., , and . J. Multivar. Anal., (2018)Flexible and efficient spatial extremes emulation via variational autoencoders., , , and . CoRR, (2023)Proteome-level assessment of origin, prevalence and function of leucine-aspartic acid (LD) motifs., , , , , , , , , and 8 other author(s). Bioinform., 36 (4): 1121-1128 (2020)At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition., , and . CoRR, (2024)