Simultaneous outlier-exclusion and distributionally robust learning through partial optimal transport
Bibliographic record
Abstract
Distributionally robust optimization (DRO) is a powerful framework that mitigates the impact of distributional uncertainty. It aims to optimize the worst-case performance over all possible distributions within an ambiguity set, defined around a nominal distribution which is often set as the empirical distribution constructed from data. However, the presence of outliers in the data may distort the construction of the ambiguity set, thereby degrading the performance of DRO. In this work, we propose an integrated approach that combines outlier exclusion and robust model training. Applying partial optimal transport, we identify and retain the subset of samples that contribute to lower model loss, effectively filtering out potential outliers that cause large losses. This retained subset is used to construct the nominal distribution for the Wasserstein DRO formulation, which addresses the residual distributional uncertainty. We derive tractable formulations for both regression and classification problems under this framework and demonstrate its effectiveness through numerical experiments and real-world chemical process datasets. The results demonstrate that the proposed method provides a simple, effective, and implementable solution for robust learning under both outlier contamination and distributional shifts.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".