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 revised arXiv preprint (2510.13311v2) introduces an unsupervised approach to detecting anomalies in tabular data by combining isolation-based techniques with spherical ensemble representations. The authors argue that existing unsupervised detectors still face fundamental limitations, and position their method for use cases such as offensive language detection, network security, and quality control. The work is a research contribution rather than a released product.
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 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.
A new arXiv paper presents a method for learning feature transformations on tabular data by searching a continuous embedding space with a learned policy. The approach is hierarchical and invariant to the ordering of input features, allowing it to generalize across feature arrangements. Its stated aim is to build informative abstractions from raw features that improve downstream predictive performance.
A revised arXiv paper challenges the widely held view that statistical methods outperform neural networks when generating differentially private tabular data. The authors argue that this conclusion glosses over cases involving densely correlated data, where neural approaches may be more effective. The submission is an updated version of previously posted research.
A new arXiv preprint investigates whether features generated by Instantaneous Quantum Polynomial-time (IQP) circuits can improve classification of credit default cases. The work frames credit default prediction as a tabular problem where even small F1 gains reduce lender exposure, and it examines the conditions under which these quantum-derived features actually help linear classifiers.
A new arXiv paper presents Literati, a method that uses AO* search to learn decision trees with optimal structures while still returning strong trees at any point during the search. It targets a known weakness of greedy top-down induction, which often produces overly complex trees on tabular data. The work joins a growing line of research on provably optimal, interpretable tree learning.