Imagen de Portada

Learning with the Nash-Sutcliffe loss

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Tyralis, Hristos, Papacharalampous, Georgia
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Methodology
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Variable transformations in consistent loss functions
por: Tyralis, Hristos, et al.
Publicado: (2025)

Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products
por: Papacharalampous, Georgia, et al.
Publicado: (2024)

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
por: Papacharalampous, Georgia, et al.
Publicado: (2024)

Loss functions arising from the index of agreement
por: Tyralis, Hristos, et al.
Publicado: (2025)

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
por: Papacharalampous, Georgia, et al.
Publicado: (2023)

A review of predictive uncertainty estimation with machine learning
por: Tyralis, Hristos, et al.
Publicado: (2022)

Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents
por: Papacharalampous, Georgia, et al.
Publicado: (2022)

Deep Huber quantile regression networks
por: Tyralis, Hristos, et al.
Publicado: (2023)

Robust low-rank estimation with multiple binary responses using pairwise AUC loss
por: Mai, The Tien
Publicado: (2026)

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
por: Liang, Muxuan, et al.
Publicado: (2024)

Exponential Lasso: robust sparse penalization under heavy-tailed noise and outliers with exponential-type loss
por: Mai, The Tien
Publicado: (2025)

Interpretable Deep Learning Methods for Multiview Learning
por: Wang, Hengkang, et al.
Publicado: (2023)

Distribution-free inference for LightGBM and GLM with Tweedie loss
por: Manna, Alokesh, et al.
Publicado: (2025)

Structured Credal Learning
por: Venkatesh, Varun, et al.
Publicado: (2026)

i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
por: Ma, Chen, et al.
Publicado: (2026)

Discrete Causal Representation Learning
por: Zhang, Wenjin, et al.
Publicado: (2026)

Learning to Partially Defer for Sequences
por: Rayan, Sahana, et al.
Publicado: (2025)

Targeted Learning for Variable Importance
por: Wang, Xiaohan, et al.
Publicado: (2024)

Contrastive Network Representation Learning
por: Dong, Zihan, et al.
Publicado: (2025)

Federated Offline Reinforcement Learning
por: Zhou, Doudou, et al.
Publicado: (2022)

Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
por: Shao, Daqian, et al.
Publicado: (2025)

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning
por: Zhou, Qin, et al.
Publicado: (2026)

Robust Learning of Heterogeneous Dynamic Systems
por: Xu, Shuoxun, et al.
Publicado: (2026)

Transfer Learning for Kernel-based Regression
por: Wang, Chao, et al.
Publicado: (2023)

Smoothness Adaptive Hypothesis Transfer Learning
por: Lin, Haotian, et al.
Publicado: (2024)

Supervised Manifold Learning for Functional Data
por: Tan, Ruoxu, et al.
Publicado: (2025)

Spatial Transfer Learning with Simple MLP
por: Yang, Hongjian
Publicado: (2024)

Interpretable Machine Learning for Survival Analysis
por: Langbein, Sophie Hanna, et al.
Publicado: (2024)

Learning from a Biased Sample
por: Sahoo, Roshni, et al.
Publicado: (2022)

Self-Supervised Learning with Gaussian Processes
por: Duan, Yunshan, et al.
Publicado: (2025)

Multi-Task Learning with Summary Statistics
por: Knight, Parker, et al.
Publicado: (2023)

Causal Effect Estimation with Learned Instrument Representations
por: Dean, Frances, et al.
Publicado: (2026)

Exact Graph Learning via Integer Programming
por: Kook, Lucas, et al.
Publicado: (2026)

Imputation Uncertainty in Interpretable Machine Learning Methods
por: Golchian, Pegah, et al.
Publicado: (2025)

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
por: Banerjee, Sudipto, et al.
Publicado: (2022)

Order-based Structure Learning with Normalizing Flows
por: Kamkari, Hamidreza, et al.
Publicado: (2023)

Causally-Aware Unsupervised Feature Selection Learning
por: Shen, Zongxin, et al.
Publicado: (2024)

Position: Benchmarking is Limited in Reinforcement Learning Research
por: Jordan, Scott M., et al.
Publicado: (2024)

Cluster Quilting: Spectral Clustering for Patchwork Learning
por: Zheng, Lili, et al.
Publicado: (2024)

Multivariate and Online Transfer Learning with Uncertainty Quantification
por: Hickey, Jimmy, et al.
Publicado: (2024)