The Sample Complexity of Multiclass and Sparse Contextual Bandits

A new study on contextual bandits in the stochastic i.i.d. setting has been released, focusing on the design of algorithms that can identify approximately optimal policies based on bandit feedback. The research highlights the $s$-sparse setting, where the reward vector's $L_1$-norm is limited, and presents a sample complexity bound that improves upon previous work by reducing dependence on the action set size.

This development is significant as it addresses a critical gap in the understanding of sample complexity in multiclass bandit problems, potentially leading to more efficient algorithms in machine learning applications and enhancing decision-making processes in various fields.