May 14, 2024, 4:42 a.m. | Daphne Theodorakopoulos (Marine Perception Research Department, German Research Center for Artificial Intelligence, Institute of Artificial Intelligen

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07640v1 Announce Type: new
Abstract: Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only care about predictive performance but also about objectives such as inference time, memory, or energy consumption. In such MOO scenarios, determining the importance of hyperparameters poses a significant challenge due to the complex interplay between the conflicting objectives. In this paper, we propose the first method for assessing the importance …

abstract analysis applications arxiv automl capabilities consumption cs.ai cs.lg energy however hyperparameter importance inference memory ml models multi-objective optimization performance pivotal predictive role type

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