Risk Metric

Risk metrics quantify the uncertainty or potential for negative outcomes in various systems, aiming to improve decision-making and safety. Current research focuses on developing and applying these metrics across diverse fields, including machine learning (using models like LSTMs and neural networks), autonomous systems (employing methods such as Monte Carlo Tree Search and control barrier functions), and financial applications (analyzing risk-adjusted returns and developing improved loss functions). These advancements have significant implications for enhancing the reliability and safety of complex systems, from autonomous vehicles to financial transactions and healthcare risk prediction.