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.

翻译：标准贝叶斯优化（BO）假设搜索空间具有均匀平滑性，这一假设在多机制问题中往往被违背，例如跨越不同能量势阱的分子构象搜索，或在异质分子骨架上进行的药物发现。单一高斯过程要么会过度平滑尖锐的转变，要么会在平滑区域误判噪声，导致不确定性校准失准。我们提出了RAMBO，一种基于狄利克雷过程混合的高斯过程模型，它能在优化过程中自动发现潜在的机制，每个机制由一个具有局部优化超参数的独立高斯过程建模。我们推导了折叠吉布斯采样方法，通过解析边缘化潜在函数实现高效推断，并引入了自适应浓度参数调度策略，以实现从粗到细的机制发现。我们的采集函数将不确定性分解为机制内与机制间两个组成部分。在合成基准测试和实际应用（包括分子构象优化、药物发现的虚拟筛选以及聚变反应堆设计）上的实验表明，该方法在多机制目标上相较于现有先进基线模型取得了持续的性能提升。