« Back to top page

Heteroscedastic Gaussian Process

Heteroscedastic Gaussian Process

HEBO (Heteroscedastic and Evolutionary Bayesian Optimisation)

Class or Function Names HEBOSampler Installation pip install -r https://hub.optuna.org/samplers/hebo/requirements.txt git clone git@github.com:huawei-noah/HEBO.git cd HEBO/HEBO pip install -e . Example search_space = { "x": FloatDistribution(-10, 10), "y": IntDistribution(0, 10), } sampler = HEBOSampler(search_space) study = optuna.create_study(sampler=sampler) See example.py for a full example. Others HEBO is the winning submission to the NeurIPS 2020 Black-Box Optimisation Challenge. Please refer to the official repository of HEBO for more details. Reference Cowen-Rivers, Alexander I., et al.