Understanding the Resilience of Neural Network Ensembles against Faulty Training Data
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
Machine learning is becoming more prevalent in safety-critical systems like autonomous vehicles and medical imaging. Faulty training data, where data is either misla-belled, missing, or duplicated, can increase the chance of misclassification, resulting in serious consequences. In this paper, we evaluate the resilience of ML ensembles against faulty training data, in order to understand how to build better ensembles. To support our evaluation, we develop a fault injection framework to systematically mutate training data, and introduce two diversity metrics that capture the distribution and entropy of predicted labels. Our experiments find that ensemble learning is more resilient than any individual model and that high accuracy neural networks are not necessarily more resilient to faulty training data. Further, we find that simple majority voting suffices in most cases for resilience in ML ensembles. Finally, we observe diminishing returns for resilience as we increase the number of models in an ensemble. These findings can help machine learning developers build ensembles that are both more resilient and more efficient.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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