ICE-T: Interactions-aware Cross-column Contrastive Embedding for Heterogeneous Tabular Datasets
Bibliographic record
Abstract
Finding high-quality representations of heterogeneous tabular datasets is crucial for their effective use in downstream machine learning tasks. Contrastive representation learning (CRL) methods have been previously shown to provide a straightforward way to learn such representations across various data domains. Current tabular CRL methods learn joint embeddings of data instances (tabular rows) by minimizing a contrastive loss between the original instance and its perturbations. Unlike existing tabular CRL methods, we propose leveraging frameworks established in multimodal representation learning, treating each tabular column as a distinct modality. A naive approach that applies a contrastive loss pairwise to tabular columns is not only prohibitively expensive as the number of columns increases, but as we demonstrate, it also fails to capture interactions between variables. Instead, we propose a novel method called ICE-T that learns each columnar embedding by contrasting it with aggregate embeddings of the complementary part of the table, thus capturing interactions and scaling linearly with the number of columns. Unlike existing tabular CRL methods, ICE-T allows for column-specific embeddings to be obtained independently of the rest of the table, enabling the inference of missing values and translation between columnar variables. We provide a comprehensive evaluation of ICE-T across diverse datasets, demonstrating that it generally surpasses the performance of the state-of-the-art alternatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".