Conservative Objective Model

Conservative objective models (COMs) are a class of machine learning methods designed to improve the reliability and safety of optimization processes, particularly in scenarios with high uncertainty or limited data. Current research focuses on developing COMs for various applications, including crystal structure prediction (using graph neural networks and variational autoencoders), offline reinforcement learning (employing count-based conservatism and model averaging), and safe robot control (leveraging energy-based constraints). The development of robust and efficient COMs holds significant promise for advancing fields like materials science, robotics, and AI safety by enabling more reliable and trustworthy decision-making in complex systems.