https://ieeexplore.ieee.org/document/8950672 2020

Scalable Hyperparameter Optimization with Lazy Gaussian Processes

https://ieeexplore.ieee.org/document/8950672 2020 cc-hpc-itwm/HPO_LazyGPR

Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy.

GAUSSIAN PROCESSES HYPERPARAMETER OPTIMIZATION

Scalable Hyperparameter Optimization with Lazy Gaussian Processes

https://ieeexplore.ieee.org/document/8950672 2020 cc-hpc-itwm/HPO_LazyGPR

Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities.

GAUSSIAN PROCESSES HYPERPARAMETER OPTIMIZATION