Novel Representative Sampling for Improved Active Learning
Why this work is in the frame
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Bibliographic record
Abstract
Active learning solves machine learning problems where acquiring labels for the data is costly. It allows for the learner to select training samples by asking intelligent questions. Various sampling strategies exist for choosing the training set for pool-based active learning. However, the existing representative querying approaches for active learning do not attempt to capture the underlying data distribution, which we believe is an important part of representative sampling. To that end, we propose an adaptation of the sigma point sampling technique from unscented transformation (UT) for constructing a representative subset. UT has shown to be very effective in non-linear transformation modeling in object tracking and robotics. When combined with the Gaussian mixture model, sigma points can estimate the statistical moments such as mean and co-variance of an unknown distribution with very few samples which are generated deterministically. Sigma point sampling being parameterized gives better control over the sampling process. We use sigma points for representative subset construction and train the learner on them. We compare our results with other sampling techniques and improve test accuracy on the handwritten digit recognition data set MNIST.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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