Training Distribution

Training distribution mismatch, where the data used to train a machine learning model differs significantly from real-world deployment data, is a major challenge hindering reliable model performance. Current research focuses on developing methods to improve model robustness to these shifts, including techniques like test-time training (adapting models during inference) and distributionally robust optimization (training models to perform well across a range of possible distributions). These approaches utilize various architectures, from simple linear models to complex deep neural networks (including CNNs, GNNs, and transformers), and are evaluated across diverse tasks and datasets. Addressing this challenge is crucial for building reliable and trustworthy machine learning systems applicable to real-world scenarios.