10 citations · 19 across the 14 of their papers we have counts for
15 papers
Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Damiano Brigo, Raphaël Huser, Dan Leonte
Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not capture the uncertainty remaining a…
deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations
Quan Vu, Xuanjie Shao, Raphaël Huser +1
Nonstationarity in spatial and spatio-temporal processes is ubiquitous in environmental datasets, but is not often addressed in practice, due to a scarcity of statistical software…
Neural Conditional Simulation for Complex Spatial Processes
Julia Walchessen, Andrew Zammit-Mangion, Raphaël Huser +1
A key objective in spatial statistics is to simulate from the distribution of a spatial process at a selection of unobserved locations conditional on observations (i.e., a predicti…
Generative modelling of multivariate geometric extremes using normalising flows
Lambert De Monte, Raphaël Huser, Ioannis Papastathopoulos +1
Leveraging the recently emerging geometric approach to multivariate extremes and the flexibility of normalising flows on the hypersphere, we propose a principled deep-learning-base…
Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
Xuanjie Shao, Jordan Richards, Raphael Huser
Modeling nonstationarity that often prevails in extremal dependence of spatial data can be challenging, and typically requires bespoke or complex spatial models that are difficult…
Separation-based causal discovery for extremes
Junshu Jiang, Jordan Richards, Raphaël Huser +1
Structural causal models (SCMs), with an underlying directed acyclic graph (DAG), provide a powerful analytical framework to describe the interaction mechanisms in large-scale comp…
Spectral Extremal Connectivity of Two-State Seizure Brain Waves
Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana +2
Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading…
Neural Parameter Estimation with Incomplete Data
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie +1
Advances in artificial intelligence (AI) and deep learning have led to neural networks being used to generate lightning-speed answers to complex science questions, paintings in the…
Extreme quantile regression with deep learning
Jordan Richards, Raphaël Huser
Estimation of extreme conditional quantiles is often required for risk assessment of natural hazards in climate and geo-environmental sciences and for quantitative risk management…
Neural Methods for Amortized Inference
Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new rev…
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Jordan Richards +1
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where es…
Deep graphical regression for jointly moderate and extreme Australian wildfires
Daniela Cisneros, Jordan Richards, Ashok Dahal +2
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, d…
Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise
Zhongwei Zhang, David Bolin, Sebastian Engelke +1
Moving average processes driven by exponential-tailed Lévy noise are important extensions of their Gaussian counterparts in order to capture deviations from Gaussianity, more flexi…
Flexible and efficient emulation of spatial extremes processes via variational autoencoders
Likun Zhang, Xiaoyu Ma, Christopher K. Wikle +1
Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit app…
Neural Bayes estimators for censored inference with peaks-over-threshold models
Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion +1
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances i…