Call For Papers

Background and Aim

The rapid development of machine learning (ML) has led to a wide range of applications in various fields, including healthcare, finance, and natural language processing. However, the success of ML models often relies on extensive expertise in feature engineering, model selection, and hyperparameter tuning. To address this issue, Automated Machine Learning (AutoML) has emerged as a promising approach to democratize AI by automating these complex processes. AutoML views the machine learning task as a configuration search problem (i.e., an optimization problem). This approach reduces the machine's reliance on human experts by enabling automatic feature processing, algorithm model selection and modeling, parameter tuning, and other tasks. AutoML has been demonstrated to either match or surpass the results of human experts manually fine-tuning parameters in numerous domains, and it can significantly lower the expenses associated with implementing and utilizing machine learning. It has become one of the most popular and cutting-edge research directions in artificial intelligence and machine learning. Recently, there has been an emerging development in the machine learning area, such as deep learning, multimodal machine learning, and Large Language Models (LLMs), that has significantly impacted the landscape of AutoML. Despite the significant progress in AutoML, many challenges and opportunities still need to be addressed.

This special session aims to provide a platform for researchers and practitioners to discuss the latest advancements in AutoML, identify the current challenges and future directions in AutoML, and promote the adoption of AutoML techniques in various domains and industries.

Scope

The topics include, but are not limited to:

• Innovative algorithms for automated feature engineering, neural architecture search (NAS), and hyperparameter optimization.

• AutoML considers multiple objectives.

• Integration of AutoML with other emerging fields, such as ensemble learning, deep learning, transfer learning, reinforcement learning, multimodal machine learning, and large language models.

• Applications of AutoML in new domains, such as edge computing, IoT, and bioinformatics.

• Empirical studies and comparative analyses of existing AutoML frameworks and tools.