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cs.LG2026
Multi-Label Proportion Learning for Sea-Ice Type Prediction
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data for this task is the ice chart…
cs.LG2026
Uncertainty-Aware Learning from Multi-Expert Interval Targets
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation acr…
cs.LG2026
Uncertainty-Aware Sea-Ice Type Mapping with Multiple Ice Charts
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice stage of development (SoD) describes the age and associated thickness of sea ice and provides important information for navigation, and operational ice monitoring. SoD labe…