Test Loss

Test loss, the difference between a model's predictions and actual values on unseen data, is a central concern in machine learning, driving research into improving model generalization and robustness. Current research focuses on developing methods to accurately estimate and minimize test loss, employing techniques like kernel ridge regression, large language models for debugging, and self-supervised learning for test-time adaptation. These advancements aim to enhance model performance and reliability across diverse applications, from software development to clinical diagnostics, by providing more accurate assessments of model generalization capabilities and identifying sources of error.