The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck. In this paper, inspired by our experience when deploying hyper-parameter tuning in a real-world application in production and the limitations of existing systems, we propose Hyper-Tune, an efficient and robust distributed hyper-parameter tuning framework. Compared with existing systems, Hyper-Tune highlights multiple system optimizations, including (1) automatic resource allocation, (2) asynchronous scheduling, and (3) multi-fidelity optimizer. We conduct extensive evaluations on benchmark datasets and a large-scale real-world dataset in production. Empirically, with the aid of these optimizations, Hyper-Tune outperforms competitive hyper-parameter tuning systems on a wide range of scenarios, including XGBoost, CNN, RNN, and some architectural hyper-parameters for neural networks. Compared with the state-of-the-art BOHB and A-BOHB, Hyper-Tune achieves up to 11.2x and 5.1x speedups, respectively.
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
University of California, Davis（加州大学戴维斯分校）；The Ohio State University（俄亥俄州立大学）；Michigan State University（密歇根州立大学）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）
L2T-Tune:LLM-Guided Hybrid Database Tuning with LHS and TD3
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Hangzhou Institute for Advanced Study, UCAS（杭州高等研究院）
Parameter Tuning of the Firefly Algorithm by Three Tuning Methods: Standard Monte Carlo, Quasi-Monte Carlo and Latin Hypercube Sampling Methods
School of Science and Technology, Middlesex University（科学与技术学院，中塞克斯大学）；Computer Engineering and Informatics, Middlesex University Dubai（计算机工程与信息学，中塞克斯大学迪拜）
