Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Position: Why Tabular Foundation Models Should Be a Research Priority
Authors: Boris Van Breugel, Mihaela Van Der Schaar
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this position piece we aim to shift the ML research community s priorities ever so slightly to a different modality: tabular data. Tabular data is the dominant modality in many fields, yet it is given hardly any research attention and significantly lags behind in terms of scale and power. We believe the time is now to start developing tabular foundation models, or what we coin a Large Tabular Model (LTM). LTMs could revolutionise the way science and ML use tabular data: not as single datasets that are analyzed in a vacuum, but contextualized with respect to related datasets. The potential impact is far-reaching: from few-shot tabular models to automating data science; from out-of-distribution synthetic data to empowering multidisciplinary scientific discovery. We intend to excite reflections on the modalities we study, and convince some researchers to study large tabular models. |
| Researcher Affiliation | Academia | Boris van Breugel 1 Mihaela van der Schaar 1 2 1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK 2Alan Turing Institute, London, UK. Correspondence to: Boris van Breugel <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | This is a position paper discussing future research directions and does not present a specific methodology with associated code. |
| Open Datasets | No | This paper is a position piece and does not conduct experiments requiring dataset access information. |
| Dataset Splits | No | This paper is a position piece and does not conduct experiments that would require dataset splits. |
| Hardware Specification | No | This paper is a position piece and does not describe experiments that would require hardware specifications. |
| Software Dependencies | No | This paper is a position piece and does not describe experiments that would require software dependencies. |
| Experiment Setup | No | This paper is a position piece and does not describe experiments or their setup. |