Online Model Selection

Online model selection focuses on dynamically choosing the best predictive model from a pool of candidates for a sequence of incoming data, aiming to minimize prediction error and computational cost. Current research emphasizes efficient algorithms, such as those based on bandit methods and meta-learning, to handle diverse model architectures, including linear models and large language models (LLMs), often incorporating contextual information and addressing decentralized data scenarios. This field is crucial for optimizing resource allocation in applications like time-series forecasting, sequential decision-making, and robotics, where selecting the most appropriate model for each task is critical for performance and efficiency.