Online Multiclass

Online multiclass learning focuses on developing algorithms that can efficiently and accurately classify data points into multiple categories as they arrive sequentially, without prior knowledge of the entire dataset. Current research emphasizes understanding the fundamental limits of learnability under various feedback scenarios (e.g., full information, bandit feedback, set-valued feedback), developing algorithms with improved regret bounds using techniques like randomized decoding and boosting, and exploring the connections between online learnability and concepts like Littlestone dimension and uniform convergence. These advancements have implications for various applications, including improving the efficiency and robustness of online prediction systems in diverse fields.