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ASAP framework enhances ML hyperparameter optimization via agent-system co-design

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ASAP, a novel agent-system co-design framework for hyperparameter optimization (HPO) in machine learning experiments. ASAP addresses limitations of existing HPO tools by integrating a diverse pool of optimizers, allowing an LLM to select proposals, and optimizing the system loop for reduced wall-clock time. This approach aims to improve sample efficiency and handle a wider range of problems compared to single-tool replacements. AI

IMPACT This framework could improve the efficiency and effectiveness of training machine learning models by optimizing hyperparameter selection.

RANK_REASON The cluster contains an arXiv preprint detailing a new research framework for machine learning experiments.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ASAP framework enhances ML hyperparameter optimization via agent-system co-design

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The cluster contains an arXiv preprint detailing a new research framework for machine learning experiments.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Taicheng Guo, Haomin Zhuang, Kehan Guo, Yujun Zhou, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang ·

    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…

  2. arXiv cs.CL TIER_1 English(EN) · Xiangliang Zhang ·

    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 …

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