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