Joint Selection

Joint selection encompasses methods for simultaneously optimizing multiple interdependent choices within a system, aiming to improve overall performance or achieve a desired outcome. Current research focuses on developing efficient algorithms, often leveraging reinforcement learning or probabilistic approaches, to select optimal combinations of elements such as data points, model modules, or agents' actions. These techniques are proving valuable across diverse fields, from accelerating machine learning training and enhancing multi-agent system cooperation to optimizing resource allocation in federated learning and improving the quality of synthetic data generation.