28 citations · 62 across the 5 of their papers we have counts for
6 papers
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
Olivier C. Pasche, Henry Lam, Sebastian Engelke
Conformal prediction is a popular method to construct prediction intervals with marginal coverage guarantees from black-box machine learning models. In applications with potentiall…
Granger Causality in Extremes
Juraj Bodik, Olivier C. Pasche
We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pi…
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang +2
The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous…
Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches
Gloria Buriticá, Manuel Hentschel, Olivier C. Pasche +2
This article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate e…
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
Olivier C. Pasche, Sebastian Engelke
Risk assessment for extreme events requires accurate estimation of high quantiles that go beyond the range of historical observations. When the risk depends on the values of observ…
Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow
Olivier C. Pasche, Valérie Chavez-Demoulin, Anthony C. Davison
Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or tak…