Fisher Information

Fisher information quantifies the amount of information an observable random variable contains about unknown parameters within a model, serving as a crucial tool in various fields, including machine learning and statistical inference. Current research focuses on leveraging Fisher information for improved optimization algorithms (e.g., AdaFisher, SOFIM), enhanced model robustness against adversarial attacks (e.g., Fisher Information guided Purification), and efficient model training and fine-tuning in large-scale settings (e.g., FedFisher, PipeFisher). These advancements have significant implications for improving the accuracy, efficiency, and privacy of machine learning models, as well as for developing a deeper theoretical understanding of their behavior.