Statistical Framework

Statistical frameworks are being developed to rigorously analyze and improve the performance and trustworthiness of various machine learning models, addressing issues like model identifiability, generalization, and bias. Current research focuses on developing principled training methods for complex architectures, such as retrieval-augmented models and large language models, and on quantifying uncertainty and bias through statistical measures. These advancements are crucial for enhancing the reliability and interpretability of machine learning systems across diverse applications, from natural language processing to wireless communication and computer vision. A key trend is the integration of statistical methods with machine learning techniques to achieve both high performance and explainability.