Building Resilient ML Applications using Ensembles against Faulty Training Data
Bibliographic record
Abstract
Supervised machine learning (ML) applications are data-driven, and thus require large amounts of data for training. Whether the training data is manually collected or automatically generated, it is prone to faults like mislabelling, accidental deletion, or repetition. Our research focuses on developing the most effective and efficient techniques with minimal human effort to improve the resilience of ML applications against faulty training data. We find that ensembles have a higher resilience to faulty training data than individual models, especially when using ensembles with architecturally diverse constituent models. We also find that ensembles are more effective than many existing techniques against mislabelled training data, even in safety-critical domains such as autonomous vehicles and healthcare. Hence, we aim to improve the applicability of ensembles in real-world systems by (1) optimizing the process of searching for resilient ensembles, and (2) reducing the training cost by maximizing reuse of trained weights and (3) examining methods to make ensembles more explainable to stakeholders.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".