Contextual bandit
A contextual bandit is a sequential decision-making method in machine learning where an algorithm observes context, chooses an action, and learns from only that action's reward, used in recommendation and advertising.
Incremental learning (machine learning)
Incremental learning trains a machine learning model by updating it continuously as new data arrives instead of retraining from a fixed dataset, with catastrophic forgetting as its central difficulty.
No-regret learning
No-regret learning is an online decision-making method where a player repeatedly chooses actions against unknown losses, with regret growing sublinearly, dating to work by Hannan and Blackwell in the 1950s.
Online continual learning
Online continual learning (OCL) is a machine learning setting in which a model learns from a continuous data stream in a single pass, retaining old tasks using a small replay buffer.
Online machine learning
Online machine learning, also called on-line learning, is a family of machine learning methods that builds predictive models by processing data one instance at a time in arrival order.