ICE-T: Interactions-aware Cross-column Contrastive Embedding for Heterogeneous Tabular Datasets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it