Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes

• URL: http://arxiv.org/abs/2601.20043v1
• Date: Tue, 27 Jan 2026 20:45:50 GMT
• Title: Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
• Authors: Yan Zhang, Xuefeng Liu, Sipeng Chen, Sascha Ranftl, Chong Liu, Shibo Li,
• Abstract summary: RAMBO is a Dirichlet Process Mixture of Gaussian Processes that automatically discovers latent regimes during optimization.<n>We derive collapsed Gibbs sampling that analytically marginalizes latent functions for efficient inference.<n>Our acquisition functions decompose uncertainty into intra-regime and inter-regime components.
• Score: 14.367563771141592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard Bayesian Optimization (BO) assumes uniform smoothness across the search space an assumption violated in multi-regime problems such as molecular conformation search through distinct energy basins or drug discovery across heterogeneous molecular scaffolds. A single GP either oversmooths sharp transitions or hallucinates noise in smooth regions, yielding miscalibrated uncertainty. We propose RAMBO, a Dirichlet Process Mixture of Gaussian Processes that automatically discovers latent regimes during optimization, each modeled by an independent GP with locally-optimized hyperparameters. We derive collapsed Gibbs sampling that analytically marginalizes latent functions for efficient inference, and introduce adaptive concentration parameter scheduling for coarse-to-fine regime discovery. Our acquisition functions decompose uncertainty into intra-regime and inter-regime components. Experiments on synthetic benchmarks and real-world applications, including molecular conformer optimization, virtual screening for drug discovery, and fusion reactor design, demonstrate consistent improvements over state-of-the-art baselines on multi-regime objectives.

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