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feature engineering

topic2 events
papersTODAY 04:00 UTC

arXiv Paper Examines Whether Tabular Foundation Models Still Require Feature Engineering

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.

tabular-data/foundation-models/feature-engineering/in-context-learning
papersSEP 10 04:00 UTC

Policy-Guided Embedding Search Learns Feature Transformations for Tabular Data

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.

tabular-data/feature-engineering/reinforcement-learning/research