None To Optima in Few Shots: Bayesian Optimization with MDP Priors
This addresses the need for efficient black-box optimization in critical real-world domains like drug discovery and materials design, representing a novel method for a known bottleneck.
The paper tackles the problem of Bayesian Optimization (BO) being impractical for costly evaluations in applications like drug discovery by introducing the ProfBO algorithm, which uses MDP priors to model optimization trajectories from related tasks, achieving high-quality solutions with significantly fewer evaluations on real-world benchmarks such as Covid and Cancer.
Bayesian Optimization (BO) is an efficient tool for optimizing black-box functions, but its theoretical guarantees typically hold in the asymptotic regime. In many critical real-world applications such as drug discovery or materials design, where each evaluation can be very costly and time-consuming, BO becomes impractical for many evaluations. In this paper, we introduce the Procedure-inFormed BO (ProfBO) algorithm, which solves black-box optimization with remarkably few function evaluations. At the heart of our algorithmic design are Markov Decision Process (MDP) priors that model optimization trajectories from related source tasks, thereby capturing procedural knowledge on efficient optimization. We embed these MDP priors into a prior-fitted neural network and employ model-agnostic meta-learning for fast adaptation to new target tasks. Experiments on real-world Covid and Cancer benchmarks and hyperparameter tuning tasks demonstrate that ProfBO consistently outperforms state-of-the-art methods by achieving high-quality solutions with significantly fewer evaluations, making it ready for practical deployment.