An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters. - Hristos Tyralis; Demetris Koutsoyiannis; Stefanos Kozanis (Comput. Stat., 2013)

Variable Selection in Time Series Forecasting Using Random Forests. - Hristos Tyralis; Georgia Papacharalampous (Algorithms, 2017)

Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. - Hristos Tyralis; Georgia Papacharalampous; Andreas Langousis (CoRR, 2019)

Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability. - Georgia Papacharalampous; Hristos Tyralis (CoRR, 2020)

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale. - Georgia Papacharalampous; Hristos Tyralis; Ilias G. Pechlivanidis; Salvatore Grimaldi; Elena Volpi (CoRR, 2021)

Boosting algorithms in energy research: a systematic review. - Hristos Tyralis; Georgia Papacharalampous (Neural Comput. Appl., 2021)

Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms. - Hristos Tyralis; Georgia Papacharalampous; Andreas Langousis (Neural Comput. Appl., 2021)

Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms. - Hristos Tyralis; Georgia Papacharalampous; Andreas Langousis; Simon Michael Papalexiou (Remote. Sens., 2021)

A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting. - Georgia Papacharalampous; Hristos Tyralis (CoRR, 2022)

A review of probabilistic forecasting and prediction with machine learning. - Hristos Tyralis; Georgia Papacharalampous (CoRR, 2022)

Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale. - Georgia Papacharalampous; Hristos Tyralis; Anastasios D. Doulamis; Nikolaos Doulamis (CoRR, 2023)

Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data. - Georgia Papacharalampous; Hristos Tyralis; Anastasios D. Doulamis; Nikolaos Doulamis (CoRR, 2023)

Machine learning for uncertainty estimation in fusing precipitation observations from satellites and ground-based gauges. - Georgia Papacharalampous; Hristos Tyralis; Nikolaos Doulamis; Anastasios Doulamis (CoRR, 2023)

Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles. - Hristos Tyralis; Georgia Papacharalampous; Nikolaos Doulamis; Anastasios D. Doulamis (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023)

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data. - Georgia Papacharalampous; Hristos Tyralis; Nikolaos Doulamis; Anastasios Doulamis (Remote. Sens., 2023)

A review of predictive uncertainty estimation with machine learning. - Hristos Tyralis; Georgia Papacharalampous (Artif. Intell. Rev., 2024)

Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning. - Georgia Papacharalampous; Hristos Tyralis; Nikolaos Doulamis; Anastasios Doulamis (CoRR, 2024)

Uncertainty estimation in satellite precipitation spatial prediction by combining distributional regression algorithms. - Georgia Papacharalampous; Hristos Tyralis; Nikolaos Doulamis; Anastasios Doulamis (CoRR, 2024)

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data. - Georgia Papacharalampous; Hristos Tyralis; Nikolaos Doulamis; Anastasios Doulamis (Mach. Learn. Sci. Technol., 2024)

Deep Huber quantile regression networks. - Hristos Tyralis; Georgia Papacharalampous; Nilay Dogulu; Kwok P. Chun (Neural Networks, 2025)

Transformations of predictions and realizations in consistent scoring functions. - Hristos Tyralis; Georgia Papacharalampous (CoRR, 2025)

Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives. - Kwok P. Chun; Thanti Octavianti; Nilay Dogulu; Hristos Tyralis; Georgia Papacharalampous; Ryan Rowberry; Pingyu Fan; Mark Everard; Maria Francesch-Huidobro; Wellington Migliari; David M. Hannah; John Travis Marshall; et al. (WIREs Data. Mining. Knowl. Discov., 2025)