Cover Image

Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Vogel, Liane, Srinivas, Kavitha, D'Souza, Niharika, Shirai, Sola, Hassanzadeh, Oktie, Samulowitz, Horst
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Databases
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
by: Khatiwada, Aamod, et al.
Published: (2024)

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
by: Kokel, Harsha, et al.
Published: (2025)

Evaluating Joinable Column Discovery Approaches for Context-Aware Search
by: Kokel, Harsha, et al.
Published: (2025)

Can AI autonomously build, operate, and use the entire data stack?
by: Agarwal, Arvind, et al.
Published: (2025)

Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
by: Baek, Jinheon, et al.
Published: (2025)

SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
by: Kang, Inwon, et al.
Published: (2026)

Less is More: Learning Graph Tasks with Just LLMs
by: Shirai, Sola, et al.
Published: (2025)

DP-Bench: A Benchmark for Evaluating Data Product Creation Systems
by: Chowdhury, Faisal, et al.
Published: (2025)

StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation
by: Kashyap, Satyananda, et al.
Published: (2025)

Hierarchical Conditional Tabular GAN for Multi-Tabular Synthetic Data Generation
by: Ågren, Wilhelm, et al.
Published: (2024)

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
by: Li, Zuqing, et al.
Published: (2025)

FeatNavigator: Automatic Feature Augmentation on Tabular Data
by: Liang, Jiaming, et al.
Published: (2024)

RFOD: Random Forest-based Outlier Detection for Tabular Data
by: Ang, Yihao, et al.
Published: (2025)

DPDisc: From Factoid Questions to Data Product Requests for Open-World Data Product Discovery over Tables and Text
by: Zhang, Liangliang, et al.
Published: (2025)

Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs
by: Tang, Yafeng, et al.
Published: (2025)

On Learning Representations for Tabular Data Distillation
by: Kang, Inwon, et al.
Published: (2025)

SemPipes -- Optimizable Semantic Data Operators for Tabular Machine Learning Pipelines
by: Ovcharenko, Olga, et al.
Published: (2026)

Systematic Assessment of Tabular Data Synthesis
by: Du, Yuntao, et al.
Published: (2024)

C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation
by: Xu, Sijia, et al.
Published: (2026)

GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions
by: Fallahian, Mohammadali, et al.
Published: (2022)

CuTS: Customizable Tabular Synthetic Data Generation
by: Vero, Mark, et al.
Published: (2023)

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
by: Liu, Xinyuan, et al.
Published: (2025)

TabularMark: Watermarking Tabular Datasets for Machine Learning
by: Zheng, Yihao, et al.
Published: (2024)

Robust Detection of Synthetic Tabular Data under Schema Variability
by: Kindji, G. Charbel N., et al.
Published: (2025)

Cross-table Synthetic Tabular Data Detection
by: Kindji, G. Charbel N., et al.
Published: (2024)

DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
by: Wen, Yizhu, et al.
Published: (2024)

Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
by: Wu, Xiaofeng, et al.
Published: (2025)

Benchmarking the Fidelity and Utility of Synthetic Relational Data
by: Hudovernik, Valter, et al.
Published: (2024)

TableGPT2: A Large Multimodal Model with Tabular Data Integration
by: Su, Aofeng, et al.
Published: (2024)

Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
by: Cui, Lingxi, et al.
Published: (2024)

Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
by: Klein, Tassilo, et al.
Published: (2025)

PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
by: Hu, Weihua, et al.
Published: (2024)

Learning from Anonymized and Incomplete Tabular Data
by: Lange, Lucas, et al.
Published: (2026)

Predictive Query-based Pipeline for Graph Data
by: Neto, Plácido A Souza
Published: (2024)

Synthetic Tabular Data Detection In the Wild
by: Kindji, G. Charbel N., et al.
Published: (2025)

MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time
by: Phan, Peter, et al.
Published: (2025)

Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data
by: Papadias, Serafeim, et al.
Published: (2026)

WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos
by: Wu, Zhaomin, et al.
Published: (2025)

Boosting Relational Deep Learning with Pretrained Tabular Models
by: Lachi, Veronica, et al.
Published: (2025)

Tabular Foundation Models Can Learn Association Rules
by: Karabulut, Erkan, et al.
Published: (2026)