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Gradient-based Sample Selection for Faster Bayesian Optimization
We propose GSSBO, a gradient-based sample selection method that scales Bayesian Optimization to large-budget scenarios by selecting a representative subset of historical observations based on gradient information.
Qiyu Wei 魏启宇, Haowei Wang, Zirui Cao, Songhao Wang, Richard Allmendinger, Mauricio A. Álvarez
arXiv
Gradient-based Sample Selection for Faster Bayesian Optimization
A job scheduling algorithm based on parallel workload prediction on computational grid
We propose AWAS, an application- and workload-aware job scheduling algorithm that uses parallel workload prediction to balance load across the computing nodes of large-scale computational grids.
Xiaoyong Tang, Yi Liu, Tan Deng, Zexin Zeng, Haowei Huang, Qiyu Wei 魏启宇, Xiaorong Li, Li Yang
A job scheduling algorithm based on parallel workload prediction on computational grid