Sequential Selection

Sequential selection focuses on optimally choosing a subset of items from a larger set in a series of steps, aiming to maximize a desired outcome while minimizing cost or time. Current research explores this across diverse applications, employing methods like Bayesian frameworks, reinforcement learning, and deep learning architectures (including convolutional neural networks and transformers) to guide the selection process. These advancements improve efficiency and accuracy in various fields, from code generation and continual learning to sensor placement and anomaly detection, ultimately leading to more effective and resource-conscious solutions.