English(EN) ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

ASAP框架通过代理-系统协同设计增强机器学习超参数优化

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-23 22:00

研究人员开发了ASAP，一种用于机器学习实验中超参数优化（HPO）的新型代理-系统协同设计框架。ASAP通过整合多样化的优化器池、允许LLM选择建议以及优化系统循环以减少挂钟时间，解决了现有HPO工具的局限性。与单一工具替代方案相比，这种方法旨在提高样本效率并处理更广泛的问题。 AI

影响该框架通过优化超参数选择，可以提高训练机器学习模型的效率和有效性。

排序理由该集群包含一篇arXiv预印本，详细介绍了用于机器学习实验的新研究框架。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CL TIER_1 English(EN) · Taicheng Guo, Haomin Zhuang, Kehan Guo, Yujun Zhou, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang · 2026-06-25 04:00

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

arXiv:2606.25207v1 Announce Type: cross Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget. Because every HPO tool relies on…
arXiv cs.CL TIER_1 English(EN) · Xiangliang Zhang · 2026-06-23 22:00

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget. Because every HPO tool relies on a surrogate prior that imparts its own inductive …