[2Win5] Poster session 2

[2Win5-10] High-dimensional Bayesian optimization algorithm using linear bandits

Efficient Optimization Using Key Parameter Extraction with Bandit and Implementation Examples

〇Yusuke Nomura¹, Koji Tabata¹, Tamiki Komatsuzaki¹ (1.Hokkaido University)

Keywords:Baysian Optimization, Bandit

This paper proposes a novel high-dimensional Bayesian optimization framework that combines a linear bandit approach for direction selection with Gaussian process-based one-dimensional searches. Standard Bayesian optimization often faces severe computational challenges in dimensions exceeding ten, due to the “curse of dimensionality” and the difficulty of directly optimizing high-dimensional acquisition functions. To mitigate these issues, our method—Directional Bandit BO—automatically learns effective search directions via a linear bandit (LinUCB) formulation, which treats each potential direction vector as an arm. By defining the bandit’s reward based on the discrepancy between the Gaussian process prediction and the true function value, the algorithm actively explores under-modeled directions. Once a direction is chosen, a one-dimensional Bayesian optimization step refines the search along that axis. We demonstrate the effectiveness of this approach on a 50-dimensional Styblinski-Tang function with only five influential dimensions. Experimental results show faster convergence compared to other high-dimensional methods, validating our proposed strategy.