Download PDFOpen PDF in browserAutomated Machine Learning: Advances in Model Selection and Hyperparameter OptimizationEasyChair Preprint 123557 pages•Date: March 1, 2024AbstractThis paper presents recent advances in AutoML, focusing on techniques for selecting models and tuning hyperparameters efficiently and effectively. Various approaches, including Bayesian optimization, genetic algorithms, and neural architecture search, are explored for automating these tasks. Moreover, the challenges and opportunities associated with adopting AutoML are discussed, including scalability, interpretability, and computational resource requirements. Finally, future research directions and potential applications of AutoML in accelerating the development and deployment of machine learning models across diverse domains are highlighted. Keyphrases: Hyperparameter, model, selection
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