Optimizing Hyperparameters with Conformal Quantile Regression
This is an incremental improvement for machine learning practitioners needing faster hyperparameter tuning.
The paper tackled hyperparameter optimization by using conformalized quantile regression to model the target function with minimal noise assumptions, resulting in quicker convergence on benchmarks.
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be warranted in practice. In this work, we propose to leverage conformalized quantile regression which makes minimal assumptions about the observation noise and, as a result, models the target function in a more realistic and robust fashion which translates to quicker HPO convergence on empirical benchmarks. To apply our method in a multi-fidelity setting, we propose a simple, yet effective, technique that aggregates observed results across different resource levels and outperforms conventional methods across many empirical tasks.