AI RESEARCH
In-Context Decision Making for Optimizing Complex AutoML Pipelines
arXiv CS.LG
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ArXi:2508.13657v2 Announce Type: replace Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the advancements of pre-trained models, modern ML workflows go beyond hyperparameter optimization and often require fine-tuning, ensembling, and other adaptation techniques. While the core challenge of identifying the best-performing model for a downstream task remains, the increasing heterogeneity of ML pipelines demands novel AutoML approaches.