BOIDS: High-dimensional Bayesian Optimization via Incumbent-guided Direction Lines and Subspace Embeddings

• URL: http://arxiv.org/abs/2412.12918v1
• Date: Tue, 17 Dec 2024 13:51:24 GMT
• Title: BOIDS: High-dimensional Bayesian Optimization via Incumbent-guided Direction Lines and Subspace Embeddings
• Authors: Lam Ngo, Huong Ha, Jeffrey Chan, Hongyu Zhang,
• Abstract summary: We introduce BOIDS, a novel high-dimensional BO algorithm that guides optimization by a sequence of one-dimensional direction lines. We also propose an adaptive selection technique to identify most optimal lines for each round of line-based optimization. Our experimental results show that BOIDS outperforms state-of-the-art baselines on various synthetic and real-world benchmark problems.
• Score: 14.558601519561721
• License:
• Abstract: When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of dimensions, hence scaling BO to high dimension is of much interest. However, state-of-the-art high-dimensional BO methods still suffer from the curse of dimensionality, highlighting the need for further improvements. In this work, we introduce BOIDS, a novel high-dimensional BO algorithm that guides optimization by a sequence of one-dimensional direction lines using a novel tailored line-based optimization procedure. To improve the efficiency, we also propose an adaptive selection technique to identify most optimal lines for each round of line-based optimization. Additionally, we incorporate a subspace embedding technique for better scaling to high-dimensional spaces. We further provide theoretical analysis of our proposed method to analyze its convergence property. Our extensive experimental results show that BOIDS outperforms state-of-the-art baselines on various synthetic and real-world benchmark problems.

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