Graph Bayesian Optimization: Algorithms, Evaluations and Applications
Network structure optimization is a fundamental task in complex network analysis. However, almost all the research on Bayesian optimization is aimed at optimizing the objective functions with vectorial inputs. In this work, we first present a flexible framework, denoted graph Bayesian optimization, to handle arbitrary graphs in the Bayesian optimization community. By combining the proposed framework with graph kernels, it can take full advantage of implicit graph structural features to supplement explicit features guessed according to the experience, such as tags of nodes and any attributes of graphs. The proposed framework can identify which features are more important during the optimization process. We apply the framework to solve four problems including two evaluations and two applications to demonstrate its efficacy and potential applications.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationSimilar Papers 제목 키워드 기반
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algo…
AutoMLBayesian Optimizationglobal-optimizationHyperparameter OptimizationThe Parallel Knowledge Gradient Method for Batch Bayesian Optimization
In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment…
Bayesian OptimizationBayesian Optimization on Networks
This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization alg…
Budgeted Batch Bayesian Optimization With Unknown Batch Sizes
Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially expensive in large parameter spaces, and …
Bayesian OptimizationBIG-bench Machine LearningExperimental Designglobal-optimizationAutomated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to au…
Bayesian OptimizationHyperparameter Optimization