Jul 14, 2023 · Despite its popularity, existing analyses of GP-UCB give a suboptimal regret rate, which fails to be sublinear for many commonly used kernels ...
Oct 16, 2023 · The study reveals that GP-UCB, when properly regularized, provides nearly optimal, sublinear regret for kernels experiencing polynomial eigendecay, including ...
In this work, we show that GP-UCB obtains almost optimal, sublinear regret for any kernel experi- encing polynomial eigendecay. This, in particular, implies ...
May 30, 2024 · Despite its popularity, existing analyses of GP-UCB give a suboptimal regret rate, which fails to be sublinear for many commonly used kernels ...
Aug 14, 2023 · In this work, we show that GP-UCB obtains almost optimal, sublinear regret for any kernel experi- encing polynomial eigendecay. This, in ...
Sep 12, 2024 · Despite its popularity, existing analyses of GP-UCB give a suboptimal regret rate, which fails to be sublinear for many commonly used kernels ...
To that end, we leverage kernel approximation techniques to prove a sublinear regret bound, which is the first (frequentist) sublinear regret guarantee on ...
Our main contribution is to derive the first sub-linear regret bounds for this problem. We numerically compare SGP-UCB against existing safe Bayesian. GP ...
In the following, we will prove that in many practical applications,. CGP-UCB attains sublinear contextual regret (i.e., is able to compete with the optimal ...
Despite its popularity, existing analyses of GP-UCB give a suboptimal regret rate, which fails to be sublinear for many commonly used kernels such as the Mat\' ...