ROBUST MACHINE LEARNING USING SUPERQUANTILES
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
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.
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.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.002 | 0.021 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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