Class Conditional

Class-conditional methods in machine learning focus on modeling the probability distribution of data within individual classes, enabling more nuanced handling of class imbalances, noisy labels, and out-of-distribution samples. Current research emphasizes robust learning techniques, often incorporating normalizing flows, Bayesian methods, or energy-based models, to improve classification accuracy and uncertainty quantification, particularly in high-dimensional spaces. These advancements are crucial for building reliable and trustworthy machine learning systems across diverse applications, from fraud detection to medical diagnostics and robotics, where accurate classification under challenging data conditions is paramount.