Tables are a promising modality for representation learning with too much application potential to ignore. However, tables have long been overlooked despite their dominant presence in the data landscape, e.g. data management and analysis pipelines. The majority of datasets in Google Dataset Search, for example, resembles typical tabular file formats like CSVs. Similarly, the top-3 most-used database management systems are all relational (RDBMS). Representation learning over tables (TRL), possibly combined with other modalities such as text or SQL, has shown impressive performance for tasks like table-based question answering, table understanding, and data preparation. More recently, TRL was shown to be effective for tabular ML as well, while researchers also started exploring the impressive capabilities of LLMs for table encoding and data manipulation. Follow our Twitter feed for updates: https://twitter.com/TrlWorkshop.

The first edition of the Table Representation Learning (TRL) workshop at NeurIPS 2022 gathered an enthusiastic community and stimulated new research and collaborations, which we aim to continue in 2023. The TRL workshop has three main goals:

(1) Motivate tables as a primary modality for representation and generative learning and advance the area further.
(2) Showcase impactful applications of pretrained table models and discussing future opportunities.
(3) Foster discussion and collaboration across the ML, NLP and DB communities.