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

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.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2601.20043 [cs.LG]

(or arXiv:2601.20043v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.20043 Focus to learn more arXiv-issued DOI via DataCite

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