Online Meta Learning

Online meta-learning focuses on developing algorithms that can rapidly adapt to new tasks using streaming data, effectively "learning to learn" in dynamic environments. Current research emphasizes addressing challenges like handling non-stationary data distributions, detecting task boundaries, and incorporating fairness constraints into the learning process, often employing adaptive weighting schemes and novel meta-update mechanisms within online learning frameworks. This field is significant because it enables more robust and efficient machine learning systems for applications like continual learning, federated learning, and adaptive control, improving performance and reducing computational costs.