Sample Loss

Sample loss, the difference between a model's predicted output and the true value, is a central concern in machine learning, driving efforts to improve model accuracy and robustness. Current research focuses on mitigating the impact of noisy or imbalanced data on sample loss, employing techniques like sample selection based on surrogate models (e.g., vision-language models) and adaptive loss functions, as well as developing theoretically grounded methods for pruning decision trees to reduce out-of-sample loss. These advancements are crucial for enhancing the reliability and generalizability of machine learning models across diverse applications, particularly in scenarios with imperfect or incomplete data.