2012/02/09 by Il Memming Park, Park, Il Memming, Marcel Nassar +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Methodology (stat.ME) #Target Tracking and Data Fusion in Sensor Networks #cs.LG #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1202.2143
arxiv created 2012/02/09 · openalex publication_date 2012/02/09 · arxiv updated 2012/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The ultimate goal of optimization is to find the minimizer of a target function.However, typical criteria for active optimization often ignore the uncertainty about the minimizer. We propose a novel criterion for global optimization and an associated sequential active learning strategy using Gaussian processes.Our criterion is the reduction of uncertainty in the posterior distribution of the function minimizer. It can also flexibly incorporate multiple global minimizers. We implement a tractable approximation of the criterion and demonstrate that it obtains the global minimizer accurately compared to conventional Bayesian optimization criteria.