Efficient and Scalable Batch Bayesian Optimization Using K-Means
We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sampling algorithm for Bayesian Optimization (BO). KMBBO uses unsupervised learning to efficiently estimate peaks of the model acquisition function. We show in empirical experiments that our method outperforms the current state-of-the-art batch allocation algorithms on a variety of test problems including tuning of algorithm hyper-parameters and a challenging drug discovery problem. In order to accommodate the real-world problem of high dimensional data, we propose a modification to KMBBO by combining it with compressed sensing to project the optimization into a lower dimensional subspace. We demonstrate empirically that this 2-step method outperforms algorithms where no dimensionality reduction has taken place.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian Optimizationcompressed sensingDimensionality ReductionDrug DiscoverySimilar Papers 제목 키워드 기반
B3O: Scalable Boltzmann Batch Bayesian Optimization
Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO). Existing batch BO methods, however, incur large computational cost or …
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems. However, most existing surrogate-assisted multi-objective optimization algorithms have th…
Bayesian OptimizationTS-RSR: A provably efficient approach for batch Bayesian Optimization
This paper presents a new approach for batch Bayesian Optimization (BO) called Thompson Sampling-Regret to Sigma Ratio directed sampling (TS-RSR), where we sample a new batch of actions by minimizing a Thompson Sampling …
Bayesian OptimizationThompson SamplingPretrained Joint Predictions for Scalable Batch Bayesian Optimization of Molecular Designs
Batched synthesis and testing of molecular designs is the key bottleneck of drug development. There has been great interest in leveraging biomolecular foundation models as surrogates to accelerate this process. In this w…
Drug DiscoveryAttentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
Physics-informed dynamical system models form critical components of digital twins of the built environment. These digital twins enable the design of energy-efficient infrastructure, but must be properly calibrated to ac…
Bayesian Optimization