Why this work is in the frame
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Bibliographic record
Abstract
A recent work [1] proposed a novel Group Sparse Classifier (GSC) that was based on the assumption that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The Group Sparse Classifier requires solving an NP hard group-sparsity promoting optimization problem. Thus a convex relaxation of the optimization problem was proposed. The convex optimization problem, however, needs to be solved by quadratic programming and hence requires a large amount of computational time. To overcome this, we propose novel greedy (sub-optimal) algorithms for directly addressing the NP hard minimization problem. We call the classifiers based on these greedy group sparsity promoting algorithms as Fast Group Sparse Classifiers (FGSC). This work shows that the FGSC has nearly the same accuracy (at 95% confidence level) as the GSC, but with much faster computational speed (nearly two orders of magnitude). When certain conditions hold the GSC and the FGSC are robust to dimensionality reduction via random projection. By robust, we mean that the classification accuracy is approximately the same before and after random projection. The robustness of these classifi ers will be theoretically proved, and will be validated by thorough experimentation.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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