Sample Based

Sample-based methods are increasingly used in machine learning to analyze and improve model performance, focusing on leveraging individual training examples to gain insights. Current research explores applications ranging from model explainability (identifying influential training points) and knowledge distillation (aligning teacher and student model outputs sample-wise) to generative model assessment (comparing sample distributions) and uncertainty quantification (improving reliability in tasks like misinformation detection). These techniques offer valuable tools for enhancing model transparency, efficiency, and robustness across diverse applications, including image classification, natural language processing, and reinforcement learning.