论文标题
ACHO：自适应共形高参数优化
ACHO: Adaptive Conformal Hyperparameter Optimization
论文作者
论文摘要
在过去的十年中，出现了一些用于超参数搜索的新型框架，但大多数依赖于严格的，通常是正常的分布假设，从而限制了搜索模型的灵活性。本文提出了一个基于共形置信区间上置信度结合采样的新型优化框架，其交换性的假设较弱，可以更好地选择搜索模型体系结构。在对随机森林和卷积神经网络的高参数搜索中探索和基准测试了几个这样的架构，显示出令人满意的间隔覆盖范围和出色的调整性能与随机搜索。
Several novel frameworks for hyperparameter search have emerged in the last decade, but most rely on strict, often normal, distributional assumptions, limiting search model flexibility. This paper proposes a novel optimization framework based on upper confidence bound sampling of conformal confidence intervals, whose weaker assumption of exchangeability enables greater choice of search model architectures. Several such architectures were explored and benchmarked on hyperparameter search of random forests and convolutional neural networks, displaying satisfactory interval coverage and superior tuning performance to random search.
