Out-of-Distribution Aware Classification for Tabular Data
Bibliographic record
Abstract
Out-of-distribution (OOD) aware classification aims to classify in-distribution samples into their respective classes while simultaneously detecting OOD samples. Previous works have largely focused on the image domain, where images from an unrelated dataset can serve as auxiliary OOD training data. In this work, we address OOD-aware classification for tabular data, where an unrelated dataset cannot be used as OOD training data. A potential solution to OOD-aware classification involves filtering out OOD samples using an outlier detection method and classifying the remaining samples with a traditional classification model. However, seamlessly integrating this approach into downstream optimization tasks is challenging due to the employment of multiple methods. Our approach is turning OOD-aware classification into traditional classification by augmenting the in-distribution training data with synthesized OOD data. This approach continues leveraging traditional classification methods while detecting OOD samples, and the learned model retains the same mathematical properties as traditional classification models, thus, it can be easily integrated into downstream tasks. We evaluate these benefits empirically using real-life datasets. Code is available at https://github.com/ah-ansari/OCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".