On the Suboptimality of GP-UCB under Polynomial Effective Optimism
A new paper published on arXiv investigates the limitations of the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm. Researchers have established upper bounds on its cumulative regret, but this work explores whether GP-UCB is truly minimax optimal. The study introduces a new regret lower bound for GP-UCB with Matérn kernels, indicating that polynomial growth in the effective optimism level hinders optimal regret rates. AI
IMPACT Identifies a fundamental limitation in a widely used optimization algorithm, potentially guiding future research towards more optimal methods.