Confidence Learning

Confidence learning in machine learning aims to improve model robustness and accuracy by explicitly modeling the uncertainty of predictions. Current research focuses on integrating confidence estimation into various model architectures, including neural networks, to address challenges like noisy data, biased labels, and limited annotations in diverse applications such as medical image segmentation and suicidal ideation prediction. This approach enhances model reliability and fairness, particularly beneficial in high-stakes domains where accurate and trustworthy predictions are crucial. The resulting improvements in model performance and interpretability have significant implications for both scientific understanding and real-world applications.