Nearly Optimal Algorithms for Adversarial Kernelized Bandits
The research develops kernelized bandits operating against an adaptive adversary by proposing Kernelized Exp3 and its efficient variant, RLS-kernelized Exp3. These methods provide a near-optimal regret bound of O(sqrt(T * gamma_T)) against any adversary, proving that these algorithms are highly competitive in complex adversarial settings.