ROBUST MACHINE LEARNING USING SUPERQUANTILES
Bibliographic record
Abstract
The proliferation of machine learning in image recognition and natural language processing applications comes with increasing risk of adversarial attacks. Such attacks can potentially spoof automated detection systems in our drones or defeat facial recognition systems and bypass automated security systems. Typical defense techniques involve long training times, which would not be viable in an operational setting. The thesis utilizes a novel superquantile-based formulation to train machine learning systems to make them more robust to noise and adversarial attacks, while incurring less training costs compared to typical adversarial training techniques. The concept is explored in the context of support vector machines and achieves similar results as in the case of L1-regularization models. Subsequently, the concept is developed for neural network training with robustness tests on commonly referenced Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research–10 classes (CIFAR-10) datasets. The test results demonstrate robustness against random noise perturbations and benchmark against typical adversarial training shows comparable results. This initial excursion into superquantile training sets the foundation for further exploration into improving machine learning robustness within less computation time.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".