Given a Hyperparameter Optimization(HPO) problem, how to design an algorithm to find optimal configurations efficiently? Bayesian Optimization(BO) and the multi-fidelity BO methods employ surrogate models to sample configurations based on history evaluations. More recent studies obtain better performance by integrating BO with HyperBand(HB), which accelerates evaluation by early stopping mechanism. However, these methods ignore the advantage of a suitable evaluation scheme over the default HyperBand, and the capability of BO is still constrained by skewed evaluation results. In this paper, we propose FlexHB, a new method pushing multi-fidelity BO to the limit as well as re-designing a framework for early stopping with Successive Halving(SH). Comprehensive study on FlexHB shows that (1) our fine-grained fidelity method considerably enhances the efficiency of searching optimal configurations, (2) our FlexBand framework (self-adaptive allocation of SH brackets, and global ranking of configurations in both current and past SH procedures) grants the algorithm with more flexibility and improves the anytime performance. Our method achieves superior efficiency and outperforms other methods on various HPO tasks. Empirical results demonstrate that FlexHB can achieve up to 6.9X and 11.1X speedups over the state-of-the-art MFES-HB and BOHB respectively.
CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization
School of CS \& Key Lab of High Confidence Software Technologies (MOE), Peking University Beijing China；School of CS \& Beijing Key Laboratory of Software；Hardware Cooperative Artificial Intelligence Systems, Peking University Beijing China；School of CS \& Key Lab of High Confidence Software Technologies (MOE), Peking University；Hardware Cooperative Artificial Intelligence Systems, Peking University
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
TU Dortmund University（图卢兹大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；RWTH Aachen University（亚琛工业大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）；University of Freiburg（弗赖堡大学）；ELLIS Institute Tübingen（图宾根ELLIS研究所）；L3S Research Center（L3S研究所以）
Mirror Descent Using the Tempesta Generalized Multi-parametric Logarithms
Systems Research Institute of Polish Academy of Science（波兰科学院系统研究所）；Nicolaus Copernicus University in Torun (UMK)（托伦 Nicolaus Copernicus 大学）；Global Innovation Research Institute of Tokyo University of Agriculture and Technology（东京大学农业技术大学校全球创新研究所）；Riken AIP（理化学研究所AIP）
FARF: A Fair and Adaptive Random Forests Classifier
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University of Waikato（怀卡托大学）；Télécom Paris, Institut Polytechnique de Paris（巴黎高等电信学院，巴黎综合理工学院）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Carnegie Mellon University（卡内基梅隆大学）；L3S Research Center（L3S研究中心）；Leibniz University Hannover（莱布尼茨汉诺威大学）
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Artificial Intelligence Research Institute（四川省人工智能研究院）；Duke Kunshan University（昆山杜克大学）
