Cover Image

Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Richards, Jordan, Huser, Raphaël
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
by: Shao, Xuanjie, et al.
Published: (2025)

Neural Bayes estimators for censored inference with peaks-over-threshold models
by: Richards, Jordan, et al.
Published: (2023)

Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)

Deep graphical regression for jointly moderate and extreme Australian wildfires
by: Cisneros, Daniela, et al.
Published: (2023)

Extreme quantile regression with deep learning
by: Richards, Jordan, et al.
Published: (2024)

Separation-based causal discovery for extremes
by: Jiang, Junshu, et al.
Published: (2025)

Simulation-based inference via telescoping ratio estimation for trawl processes
by: Leonte, Dan, et al.
Published: (2025)

Generative modelling of multivariate geometric extremes using normalising flows
by: De Monte, Lambert, et al.
Published: (2025)

Neural Bayes estimation and selection for complex bivariate extremal dependence models
by: André, Lídia M., et al.
Published: (2025)

An interpretable neural network-based non-proportional odds model for ordinal regression
by: Okuno, Akifumi, et al.
Published: (2023)

Deep learning joint extremes of metocean variables using the SPAR model
by: Mackay, Ed, et al.
Published: (2024)

Neural Conditional Simulation for Complex Spatial Processes
by: Walchessen, Julia, et al.
Published: (2025)

Neural Parameter Estimation with Incomplete Data
by: Sainsbury-Dale, Matthew, et al.
Published: (2025)

Flexible and efficient emulation of spatial extremes processes via variational autoencoders
by: Zhang, Likun, et al.
Published: (2023)

Flexible Modeling of Nonstationary Extremal Dependence using Spatially-Fused LASSO and Ridge Penalties
by: Shao, Xuanjie, et al.
Published: (2022)

Automatic debiasing of neural networks via moment-constrained learning
by: Hines, Christian L., et al.
Published: (2024)

Integrating Uncertainty Awareness into Conformalized Quantile Regression
by: Rossellini, Raphael, et al.
Published: (2023)

Statistics of extremes for natural hazards: landslides and earthquakes
by: Yadav, Rishikesh, et al.
Published: (2024)

Generative neural networks for characteristic functions
by: Brück, Florian
Published: (2024)

Deep Learning of Multivariate Extremes via a Geometric Representation
by: Murphy-Barltrop, Callum J. R., et al.
Published: (2024)

A least distance estimator for a multivariate regression model using deep neural networks
by: Shin, Jungmin, et al.
Published: (2024)

Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes
by: Huser, Raphaël, et al.
Published: (2024)

Computing high-dimensional optimal transport by flow neural networks
by: Xu, Chen, et al.
Published: (2023)

Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
by: Braun, Sacha, et al.
Published: (2026)

Semi-parametric bulk and tail regression using spline-based neural networks
by: Majumder, Reetam, et al.
Published: (2025)

Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
by: Jantre, Sanket, et al.
Published: (2023)

Causal Spatial Quantile Regression
by: Gong, Yan, et al.
Published: (2025)

deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations
by: Vu, Quan, et al.
Published: (2025)

Nonparametric efficient inference for network quantile causal effects under partial interference
by: Cheng, Chao, et al.
Published: (2026)

Joint modeling of low and high extremes using a multivariate extended generalized Pareto distribution
by: Alotaibi, Noura, et al.
Published: (2025)

Central limit theorems for the outputs of fully convolutional neural networks with time series input
by: Betken, Annika, et al.
Published: (2026)

Assessing the overall and partial causal well-specification of nonlinear additive noise models
by: Schultheiss, Christoph, et al.
Published: (2023)

Spatio-temporal modeling and forecasting with Fourier neural operators
by: Nag, Pratik, et al.
Published: (2026)

Learning to Simulate: Generative Metamodeling via Quantile Regression
by: Hong, L. Jeff, et al.
Published: (2023)

Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
by: Talento, Mara Sherlin, et al.
Published: (2025)

Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
by: Matabuena, Marcos, et al.
Published: (2024)

Simulation-Based Inference via Regression Projection and Batched Discrepancies
by: Farahi, Arya, et al.
Published: (2026)

Mixture of partially linear experts
by: Hwang, Yeongsan, et al.
Published: (2024)

Conditional Rank-Rank Regression via Deep Conditional Transformation Models
by: Wang, Xiaoyi, et al.
Published: (2026)

Enhancing Differentially Private Linear Regression via Public Second-Moment
by: Cao, Zilong, et al.
Published: (2025)