The optimization of expensive black-box functions is ubiquitous in science and engineering. A common solution to this problem is Bayesian optimization (BO), which is generally comprised of two components: (i) a surrogate model and (ii) an acquisition function, which generally require expensive re-training and optimization steps at each iteration, respectively. Although recent work enabled in-context surrogate models that do not require re-training, virtually all existing BO methods still require acquisition function maximization to select the next observation, which introduces many knobs to tune, such as Monte Carlo samplers and multi-start optimizers. In this work, we propose a completely in-context, zero-shot solution for BO that does not require surrogate fitting or acquisition function optimization. This is done by using a pre-trained deep generative model to directly sample from the posterior over the optimum point. We show that this process is equivalent to Thompson sampling and demonstrate the capabilities and cost-effectiveness of our foundation model on a suite of real-world benchmarks. We achieve an efficiency gain of more than 35x in terms of wall-clock time when compared with Gaussian process-based BO, enabling efficient parallel and distributed BO, e.g., for high-throughput optimization.
View on arXiv@article{carvalho2025_2505.23913,
title={ Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling },
author={ Gustavo Sutter Pessurno de Carvalho and Mohammed Abdulrahman and Hao Wang and Sriram Ganapathi Subramanian and Marc St-Aubin and Sharon O'Sullivan and Lawrence Wan and Luis Ricardez-Sandoval and Pascal Poupart and Agustinus Kristiadi },
journal={arXiv preprint arXiv:2505.23913},
year={ 2025 }
}