Labeling Error

Labeling errors, inaccuracies in the labels assigned to training data, significantly impact the performance and reliability of machine learning models across diverse applications. Current research focuses on developing methods to detect and mitigate the effects of these errors, exploring techniques like loss reweighting (e.g., Rockafellian Relaxation), improved Bayesian approaches, and the design of robust similarity learning algorithms. Addressing this pervasive issue is crucial for improving the accuracy and generalizability of machine learning models, ultimately leading to more reliable and trustworthy AI systems in various fields.