10 citations · 19 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…