Hristos Tyralis

29 papers Journal 29

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning with the Nash-Sutcliffe loss. CoRR Hristos Tyralis, Georgia Papacharalampous
2026	J	`jnl`	Variable transformations in consistent loss functions. Knowl. Based Syst. Hristos Tyralis, Georgia Papacharalampous
2025	J	`jnl`	Deep Huber quantile regression networks. Neural Networks Hristos Tyralis, Georgia Papacharalampous, Nilay Dogulu, Kwok-Pan Chun
2025	J	`jnl`	Transformations of predictions and realizations in consistent scoring functions. CoRR Hristos Tyralis, Georgia Papacharalampous
2025	J	`jnl`	Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives. WIREs Data. Mining. Knowl. Discov. Kwok-Pan 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, Rafael Tolosana-Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño-Golle, Ye Mu, Manuel Davila Delgado, Elizabeth Silvestre Espinoza, Martin Keulertz, Deepak Gopinath, Cheng Li
2024	J	`jnl`	A review of predictive uncertainty estimation with machine learning. Artif. Intell. Rev. Hristos Tyralis, Georgia Papacharalampous
2024	J	`jnl`	Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives. CoRR Kwok-Pan 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, Rafael Tolosana-Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño-Golle, Ye Mu, Manuel Delgado, Elizabeth Silvestre Espinoza, Martin Keulertz, Deepak Gopinath, Cheng Li
2024	J	`jnl`	Uncertainty estimation in satellite precipitation spatial prediction by combining distributional regression algorithms. CoRR Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
2024	J	`jnl`	Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning. CoRR Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
2024	J	`jnl`	Uncertainty estimation of machine learning spatial precipitation predictions from satellite data. Mach. Learn. Sci. Technol. Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
2023	J	`jnl`	Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data. CoRR Georgia Papacharalampous, Hristos Tyralis, Anastasios D. Doulamis, Nikolaos Doulamis
2023	J	`jnl`	Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale. CoRR Georgia Papacharalampous, Hristos Tyralis, Anastasios D. Doulamis, Nikolaos Doulamis
2023	J	`jnl`	Deep Huber quantile regression networks. CoRR Hristos Tyralis, Georgia Papacharalampous, Nilay Dogulu, Kwok-Pan Chun
2023	J	`jnl`	Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data. Remote. Sens. Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
2023	J	`jnl`	Ensemble learning for blending gridded satellite and gauge-measured precipitation data. CoRR Georgia Papacharalampous, Hristos Tyralis, Nikolaos D. Doulamis, Anastasios D. Doulamis
2023	J	`jnl`	Machine learning for uncertainty estimation in fusing precipitation observations from satellites and ground-based gauges. CoRR Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
2023	J	`jnl`	Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis, Anastasios D. Doulamis
2023	J	`jnl`	Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles. CoRR Hristos Tyralis, Georgia Papacharalampous, Nikolaos D. Doulamis, Anastasios D. Doulamis
2022	J	`jnl`	A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting. CoRR Georgia Papacharalampous, Hristos Tyralis
2022	J	`jnl`	A review of probabilistic forecasting and prediction with machine learning. CoRR Hristos Tyralis, Georgia Papacharalampous
2021	J	`jnl`	Boosting algorithms in energy research: a systematic review. Neural Comput. Appl. Hristos Tyralis, Georgia Papacharalampous
2021	J	`jnl`	Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms. Remote. Sens. Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis, Simon Michael Papalexiou
2021	J	`jnl`	Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale. CoRR Georgia Papacharalampous, Hristos Tyralis, Ilias G. Pechlivanidis, Salvatore Grimaldi, Elena Volpi
2021	J	`jnl`	Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms. Neural Comput. Appl. Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
2020	J	`jnl`	Boosting algorithms in energy research: A systematic review. CoRR Hristos Tyralis, Georgia Papacharalampous
2020	J	`jnl`	Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability. CoRR Georgia Papacharalampous, Hristos Tyralis
2019	J	`jnl`	Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. CoRR Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
2017	J	`jnl`	Variable Selection in Time Series Forecasting Using Random Forests. Algorithms Hristos Tyralis, Georgia Papacharalampous
2013	J	`jnl`	An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters. Comput. Stat. Hristos Tyralis, Demetris Koutsoyiannis, Stefanos Kozanis