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Poster
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
Sudeep Salgia · Sattar Vakili · Qing Zhao

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @
in Poster Session 1 »
We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guarantee. The proposed algorithm is rooted in the methodology of domain shrinking realized through a sequence of tree-based region pruning and refining to concentrate queries in increasingly smaller high-performing regions of the function domain. The search for high-performing regions is localized and guided by an iterative estimation of the optimal function value to ensure both learning efficiency and computational efficiency. Compared with the prevailing GP-UCB family of algorithms, the proposed algorithm reduces computational complexity by a factor of $O(T^{2d-1})$ (where $T$ is the time horizon and $d$ the dimension of the function domain).
Keywords: Optimization · Bandits · Kernel Methods

Author Information

Sudeep Salgia (Cornell University)
Sattar Vakili (MediaTek Research)
Qing Zhao (Cornell University)

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