RoBO Gaussian Process
experiment:
algorithms:
RoBO_GP:
seed: 0
n_initial_points: 20
maximizer: 'random'
acquisition_func: 'log_ei'
normalize_input: True
normalize_output: False
RoBO Gaussian Process with MCMC
experiment:
algorithms:
RoBO_GP_MCMC:
seed: 0
n_initial_points: 20
maximizer: 'random'
acquisition_func: 'log_ei'
normalize_input: True
normalize_output: False
chain_length: 2000
burnin_steps: 2000
RoBO Random Forest
experiment:
algorithms:
RoBO_RandomForest:
seed: 0
n_initial_points: 20
maximizer: 'random'
acquisition_func: 'log_ei'
num_trees: 30
do_bootstrapping: True
n_points_per_tree: 0
compute_oob_error: False
return_total_variance: True
RoBO DNGO
experiment:
algorithms:
RoBO_DNGO:
seed: 0
n_initial_points: 20
maximizer: 'random'
acquisition_func: 'log_ei'
normalize_input: True
normalize_output: False
chain_length: 2000
burnin_steps: 2000
batch_size: 10
num_epochs: 500
learning_rate: 1e-2
adapt_epoch: 5000
RoBO BOHAMIANN
experiment:
algorithms:
RoBO_BOHAMIANN:
seed: 0
n_initial_points: 20
maximizer: 'random'
acquisition_func: 'log_ei'
normalize_input: True
normalize_output: False
burnin_steps: 2000
sampling_method: "adaptive_sghmc"
use_double_precision: True
num_steps: null
keep_every: 100
learning_rate: 1e-2
batch_size: 20
epsilon: 1e-10
mdecay: 0.05
verbose: False