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An iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular prediction models that guarantees instance-level interpretability and provides a fully transparent, semantic audit trail for every prediction.
A user foundation model is presented that applies self-supervised learning on user browsing histories and shows that the learned representation improves multiple downstream production tasks, demonstrating the viability of this approach on the open web.
This survey reviews automated multimodal machine learning (MMAutoML), distinguishing core systems that automate both representation and fusion decisions from near-core systems, partial multimodal AutoML tools, and adjacent multimodal ML infrastructure.
This work introduces PromptPack, a scalable, high-throughput LLM annotation agent that cuts LLM costs by 89% and accelerates throughput by 2.5x while fully preserving AUC.
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