Safe Linear Bandit

Safe linear bandits address the challenge of maximizing rewards in sequential decision-making problems while simultaneously adhering to safety constraints, a crucial aspect in many real-world applications. Current research focuses on developing computationally efficient algorithms, such as those leveraging convex optimization and doubly-optimistic strategies, to achieve low regret (minimizing suboptimal choices) and few safety violations. These advancements are improving the performance and applicability of safe bandit algorithms across diverse fields, including healthcare and robotics, where safety is paramount. The development of tighter theoretical guarantees and improved empirical performance remains a key focus.