标题： 使用共形分位数回归优化超参数 显示英文标题

标题： Optimizing Hyperparameters with Conformal Quantile Regression

摘要： 许多最先进的超参数优化（HPO）算法依赖于基于模型的优化器，这些优化器学习目标函数的代理模型以指导搜索。高斯过程由于能够捕捉不确定性而成为事实上的代理模型，但它们对观测噪声做出了强假设，在实践中可能并不合理。在本工作中，我们提出利用校准分位数回归，该方法对观测噪声做出最少假设，并因此以更现实和稳健的方式建模目标函数，这在经验基准上转化为更快的HPO收敛。为了在多保真度设置中应用我们的方法，我们提出了一种简单但有效的技术，该技术汇总了不同资源水平的观察结果，并在许多经验任务中优于传统方法。 显示英文摘要

摘要： 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.