Kernel Bandit

Kernel bandits address the challenge of sequentially optimizing an unknown function residing in a Reproducing Kernel Hilbert Space (RKHS), aiming to minimize cumulative regret over time. Current research focuses on improving confidence bounds for algorithms like KernelUCB (GP-UCB), developing distributed and communication-efficient versions for multi-agent settings, and exploring the use of neural networks and quantum computing to enhance performance. This field is significant because it provides powerful tools for optimizing complex, non-linear reward functions in various applications, from Bayesian optimization to personalized recommendations and distributed control systems.