Meta-learning bandits represent one of the most advanced paradigms in online decision-making. Unlike traditional multi-armed bandits (MABs), where agents continuously learn from scratch, meta-learning bandits (MLB) are designed to learn how to learn across tasks. This enables fast adaptation to new environments, making them ideal for real-world applications like dynamic pricing, recommender systems, clinical trials, … Continue reading Meta-Learning Bandits: Modern Algos for Rapid Adaptation