Multiclass Learnability

Multiclass learnability investigates the theoretical conditions under which algorithms can effectively learn to classify data into multiple categories. Current research focuses on characterizing learnability through combinatorial dimensions like the DS dimension, extending beyond the traditional PAC framework to encompass online learning settings and bandit feedback, and developing efficient boosting algorithms for multiclass problems. These advancements refine our understanding of the fundamental limits of multiclass classification and inform the design of more robust and efficient machine learning models with applications across various domains.