158 citations · 341 across the 22 of their papers we have counts for
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Learning with the Nash-Sutcliffe loss
Hristos Tyralis, Georgia Papacharalampous
The Nash-Sutcliffe efficiency () is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decisi…
Variable transformations in consistent loss functions
Hristos Tyralis, Georgia Papacharalampous
The empirical use of variable transformations within (strictly) consistent loss functions is widespread, yet a theoretical understanding is lacking. To address this gap, we develop…
Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to…
Deep Huber quantile regression networks
Hristos Tyralis, Georgia Papacharalampous, Nilay Dogulu +1
Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error s…
A review of predictive uncertainty estimation with machine learning
Hristos Tyralis, Georgia Papacharalampous
Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Al…