Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression. - Souhaib Ben Taieb; Raphaël Huser; Rob J. Hyndman; Marc G. Genton (IEEE Trans. Smart Grid, 2016)

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. - Daniela Castro Camilo; Luigi Lombardo; P. Martin Mai; Jie Dou; Raphaël Huser (Environ. Model. Softw., 2017)

Hierarchical Archimax copulas. - Marius Hofert; Raphaël Huser; Avinash Prasad (J. Multivar. Anal., 2018)

A comparison of dependence function estimators in multivariate extremes. - Sabrina Vettori; Raphaël Huser; Marc G. Genton (Stat. Comput., 2018)

Proteome-level assessment of origin, prevalence and function of leucine-aspartic acid (LD) motifs. - Tanvir Alam; Meshari Alazmi; Rayan Naser; Franceline Huser; Afaque A. Momin; Veronica Astro; SeungBeom Hong; Katarzyna W. Walkiewicz; Christian G. Canlas; Raphaël Huser; Amal J. Ali; Jasmeen Merzaban; et al. (Bioinform., 2020)

A unifying partially-interpretable framework for neural network-based extreme quantile regression. - Jordan Richards; Raphaël Huser (CoRR, 2022)

Flexible and efficient spatial extremes emulation via variational autoencoders. - Likun Zhang; Xiaoyu Ma; Christopher K. Wikle; Raphaël Huser (CoRR, 2023)

Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system. - Mahnoor Ahmed; Hakan Tanyas; Raphaël Huser; Ashok Dahal; Giacomo Titti; Lisa Borgatti; Mirko Francioni; Luigi Lombardo (Int. J. Appl. Earth Obs. Geoinformation, 2023)

At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition. - Ashok Dahal; Raphaël Huser; Luigi Lombardo (CoRR, 2024)

Neural Methods for Amortised Parameter Inference. - Andrew Zammit-Mangion; Matthew Sainsbury-Dale; Raphaël Huser (CoRR, 2024)

Vecchia Likelihood Approximation for Accurate and Fast Inference with Intractable Spatial Max-Stable Models. - Raphaël Huser; Michael L. Stein; Peng Zhong (J. Comput. Graph. Stat., 2024)

Max-convolution processes with random shape indicator kernels. - Pavel Krupskii; Raphaël Huser (J. Multivar. Anal., 2024)

Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning. - Yan Gong; Peng Zhong; Thomas Opitz 001; Raphaël Huser (Technometrics, 2024)

An efficient workflow for modelling high-dimensional spatial extremes. - Silius M. Vandeskog; Sara Martino; Raphaël Huser (Stat. Comput., 2024)

Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties. - Xuanjie Shao; Arnab Hazra; Jordan Richards; Raphaël Huser (Technometrics, 2025)

A neural network-based adaptive cut-off approach to normality testing for dependent data. - Minwoo Kim; Marc G. Genton; Raphaël Huser; Stefano Castruccio (Stat. Comput., 2025)

Multi-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity. - Aamal Abbas Hussain; Dan Leonte; Francesco Belardinelli; Raphaël Huser; Dario Paccagnan (CoRR, 2025)