Agentic Search Spaces for Tabular Machine Learning

Recent developments explore agentic AI systems designing extended HPO search spaces for tabular machine learning pipelines to outperform standard configurations. Simultaneously, human-in-the-loop practices integrate human judgment into the ML lifecycle, focusing scarce attention on uncertain examples to improve model training. While traditional machine learning has limits, combining it with agentic reasoning creates more capable systems. This evolution aligns with the broader GenAI era, where tools like ChatGPT and Claude have already transformed business processes and professional workflows.