iLTM: Integrated Large Tabular Model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911276855197696 |
|---|---|
| author | Bonet, David Cara, Marçal Comajoan Calafell, Alvaro Montserrat, Daniel Mas Ioannidis, Alexander G. |
| author_facet | Bonet, David Cara, Marçal Comajoan Calafell, Alvaro Montserrat, Daniel Mas Ioannidis, Alexander G. |
| contents | Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not fully carried over to the tabular domain, where gradient-boosted decision trees (GBDTs) remain a default choice in practice. We present iLTM, an integrated Large Tabular Model that unifies tree-derived embeddings, dimensionality-agnostic representations, a meta-trained hypernetwork, multilayer perceptrons (MLPs), and retrieval within a single architecture. Pretrained on more than 1,800 heterogeneous classification datasets, iLTM achieves consistently superior performance across tabular classification and regression tasks, from small datasets to large and high-dimensional tasks. After light fine-tuning, the meta-trained hypernetwork transfers to regression targets, matching or surpassing strong baselines. Extensive experiments show that iLTM outperforms well-tuned GBDTs and leading deep tabular models while requiring less task-specific tuning. By bridging the gap between tree-based and neural methods, iLTM offers a new framework for tabular foundation models for robust, adaptable, and scalable tabular learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | iLTM: Integrated Large Tabular Model Bonet, David Cara, Marçal Comajoan Calafell, Alvaro Montserrat, Daniel Mas Ioannidis, Alexander G. Machine Learning Artificial Intelligence 68T05 (Primary) 68T07, 68T10 (Secondary) I.2.6; I.5.1; H.2.8 Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not fully carried over to the tabular domain, where gradient-boosted decision trees (GBDTs) remain a default choice in practice. We present iLTM, an integrated Large Tabular Model that unifies tree-derived embeddings, dimensionality-agnostic representations, a meta-trained hypernetwork, multilayer perceptrons (MLPs), and retrieval within a single architecture. Pretrained on more than 1,800 heterogeneous classification datasets, iLTM achieves consistently superior performance across tabular classification and regression tasks, from small datasets to large and high-dimensional tasks. After light fine-tuning, the meta-trained hypernetwork transfers to regression targets, matching or surpassing strong baselines. Extensive experiments show that iLTM outperforms well-tuned GBDTs and leading deep tabular models while requiring less task-specific tuning. By bridging the gap between tree-based and neural methods, iLTM offers a new framework for tabular foundation models for robust, adaptable, and scalable tabular learning. |
| title | iLTM: Integrated Large Tabular Model |
| topic | Machine Learning Artificial Intelligence 68T05 (Primary) 68T07, 68T10 (Secondary) I.2.6; I.5.1; H.2.8 |
| url | https://arxiv.org/abs/2511.15941 |