Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

Binnig, Carsten; Eisenschlos, Julian Martin; Hulsebos, Madelon; Hutter, Frank

doi:10.4230/DagRep.15.4.126

Document

Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

Authors Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, Frank Hutter and all authors of the abstracts in this report

Part of: Issue: Dagstuhl Reports, Volume 15, Issue 4
Part of: Volume: Dagstuhl Reports, Volume 15
Part of: Journal: Dagstuhl Reports (DagRep)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2025-12-15

PDF

File

PDF

DagRep.15.4.126.pdf

Filesize: 1.96 MB
13 pages

Document Identifiers

DOI: 10.4230/DagRep.15.4.126
URN: urn:nbn:de:0030-drops-252531

Subject Classification

ACM Subject Classification

Computing methodologies → Natural language processing
Computing methodologies → Neural networks
Information systems → Data management systems

Keywords

applications of table representation learning
benchmarks and datasets for table representation learning
pre-trained (language) models for tables and databases
representation and generative learning for data management and analysis

Metrics

Access Statistics
Total Accesses (updated on a weekly basis)

0

Document

0

Metadata

Abstract

The growing volume and importance of structured data have sparked increasing interest in Table Representation Learning (TRL), an emerging field that leverages neural models to learn abstract, general-purpose representations for tabular data to support a wide range of downstream tasks such as tabular prediction, table question answering, tabular data cleaning, and many more. This seminar gathered the different communities (ML, NLP, IR, DB) who work on this topic to discuss the challenges & long-term vision of this field.
From the organizers: Carsten Binnig, Julian Eisenschlos, Madelon Hulsebos, Frank Hutter.

Cite As Get BibTex

Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter. Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182). In Dagstuhl Reports, Volume 15, Issue 4, pp. 126-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/DagRep.15.4.126

Author Details

Carsten Binnig

TU Darmstadt, DE

Julian Martin Eisenschlos

Google Research - Zürich, CH

Madelon Hulsebos

CWI - Amsterdam, NL

Frank Hutter

Prior Labs - Freiburg, DE & ELLIS Institute Tübingen, DE & Universität Freiburg, DE

and all authors of the abstracts in this report

Any Issues?

Feedback on the Current Page

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail