Test Label Distribution

Test label distribution research focuses on improving the performance of machine learning models when the distribution of classes in the test data differs from that in the training data. Current research emphasizes developing methods to handle this discrepancy, including adapting models to unknown test distributions using techniques like mixture-of-experts and generalized importance weighting, and designing algorithms robust to variations in both global and local label distributions. Addressing this challenge is crucial for building reliable and generalizable models, particularly in applications like medical image analysis where class prevalence varies significantly across datasets and populations.