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Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search — arXiv:2609.10225

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.10225
License (as archived): CC0-1.0
Archived: 2026-10-16T07:27:01.788Z
SHA-256 of the archived text: 8f312442abb4f6280d858b543786b3da1f52eda142ca34f5d2159001e939f996
Stable URL of this page: https://metraipolis.com/documento/dc_ff2dee31f184

How to cite

Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search — arXiv:2609.10225 — arXiv (en), https://arxiv.org/abs/2609.10225 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff2dee31f184 (Metraipolis archive dc_ff2dee31f184, sha256 8f312442abb4f6280d858b543786b3da1f52eda142ca34f5d2159001e939f996)

BibTeX

@misc{dcff2dee31f184,
  title = {Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search — arXiv:2609.10225},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.10225},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff2dee31f184; sha256 8f312442abb4f6280d858b543786b3da1f52eda142ca34f5d2159001e939f996},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.10225. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 high-level abstractions; (2) enforcing order-sensitive embeddings on inherently permutation-invariant transformation sequences, thereby introducing systematic bias; and (3) relying on gradient-based search, which is ill-suited to non-convex transformation spaces. We propose a framework with two complementary components. First, a permutation-invariant hierarchical module captures interactions across features, operations, and abstraction levels, with a self-attention pooling mechanism that maps semantically equivalent structures to consistent embeddings aligned with downstream performance. Second, a policy-guided multi-objective reinforcement learning strategy initializes the search from empirically strong seeds and jointly optimizes predictive accuracy and transformation efficiency. Extensive experiments on diverse tabular benchmarks demonstrate the effectiveness and robustness of our framework against strong baselines. Our code and data are publicly available at: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-10 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.10225 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T07:00:16.395Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).