Abstract ConfOptSampler provides flexible and robust hyperparameter optimization via calibrated quantile regression surrogates.
It supports the following acquisition functions:
Thompson Sampling Optimistic Bayesian Sampling Expected Improvement It is robust to heteroskedastic, skewed, non-normal and highly categorical environments where traditional GPs might fail.
Its single-fidelity performance in popular HPO benchmarks puts it consistently ahead of TPE and SMAC and contextually ahead of GPs (when hyperparameters are categorical):
API ConfOptSampler class takes the following parameters: