{
  "id": 6523625,
  "title": "Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search",
  "url": "https://urgent.news/2026/09/09/hierarchical-and-permutation-invariant-feature-transformation",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-09T14:23:34.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.10225v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformation knowledge into continuous embedding spaces for efficient exploration of candidate strategies, but face three key limitations: (1) overlooking hierarchical relationships between low-level features, operations, and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}