Offline Adaptation

Offline adaptation focuses on improving the performance of models or algorithms using pre-collected data, without requiring online interaction with the environment. Current research explores various approaches, including model decomposition for efficient edge-cloud collaboration, reinforcement learning techniques that balance exploration and exploitation of offline datasets, and methods for mitigating distribution shifts in image classification and other domains. This research is significant because it enables robust and efficient model deployment in dynamic environments where online learning is impractical or costly, impacting fields ranging from robotics and AI to transportation and process control.