A new arXiv paper examines how tabular foundation models, which perform well on standard classification and regression tasks, can be adapted to time-to-event (survival) prediction. The authors propose adaptation interfaces to address the difficulty that censored outcomes pose for in-context learning. The work is listed under both machine learning and AI categories on arXiv.
A new arXiv preprint in machine learning asks whether manual feature engineering remains necessary now that tabular foundation models exist. These models are pretrained across many tabular datasets and applied through in-context learning, which may reduce reliance on hand-crafted features. The abstract frames the question as an open issue for tabular machine learning research.
A new arXiv paper argues that deep tabular models using in-context learning can achieve broader generalization even when trained or conditioned on just one table. This challenges the common assumption that such models need many diverse tables to generalize well. The work focuses on inference-time context rather than weight updates.