Bandit Instance

Bandit instance research focuses on optimizing decision-making in scenarios where choices yield uncertain rewards, aiming to minimize cumulative regret. Current research explores algorithm robustness under various conditions, including strategic agent behavior (e.g., replication attacks), model misspecification, and non-stationary environments, employing techniques like Thompson sampling, UCB variants, and exploration-then-commit strategies. These investigations are crucial for improving the reliability and efficiency of algorithms in real-world applications ranging from online advertising and resource allocation to clinical trials and personalized recommendations. Understanding the instance-dependent complexities and developing algorithms that are robust to various challenges remains a central theme.