High-dimensional Bayesian Optimization via Condensing-Expansion Projection
Abstract: In high-dimensional settings, Bayesian optimization (BO) can be expensive and infeasible. The random embedding Bayesian optimization algorithm is commonly used to address highdimensional BO challenges. However, this method relies on the effective subspace assumption
on the optimization problem’s objective function, which limits its applicability. In this paper, we introduce Condensing-Expansion Projection Bayesian optimization (CEPBO), a novel random projection-based approach for high-dimensional BO that does not reply on the effective subspace assumption. The approach is both simple to implement and highly practical. We present two algorithms based on different random projection matrices: the Gaussian projection matrix and the hashing projection matrix. Experimental results demonstrate that both algorithms outperform existing random embedding-based algorithms in most cases, achieving superior performance on high-dimensional BO problems.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We have highlighted the changes in red for clarity.
Assigned Action Editor: ~Masashi_Sugiyama1
Submission Number: 3935
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