Towards Semantic Automated Machine learning
AutoML tools are designed to automatically find the best machine learning models for specific tasks. However, they often lack the ability to incorporate domain-specific knowledge and provide explanations for their decisions. Integrating LLMs into AutoML can offer improved semantic understanding, enabling users to communicate their expertise and preferences in natural language. This fusion has the potential to create more powerful and user-friendly AutoML tools that bridge the gap between technical experts and machine learning novices, fostering better collaboration and knowledge exchange in the development of ML solutions. The goal of the doctoral work is to introduce approaches to combine the scalability and robustness of classical ML techniques with the vast domain knowledge embedded in large language models (LLMs). This allows for human-in-the-loop, interpretable AutoML.
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Radwa El Shawi