Near Oracle Performance and Block Analysis of Signal Space Greedy\n Methods
Bibliographic record
Abstract
Compressive sampling (CoSa) is a new methodology which demonstrates that\nsparse signals can be recovered from a small number of linear measurements.\nGreedy algorithms like CoSaMP have been designed for this recovery, and\nvariants of these methods have been adapted to the case where sparsity is with\nrespect to some arbitrary dictionary rather than an orthonormal basis. In this\nwork we present an analysis of the so-called Signal Space CoSaMP method when\nthe measurements are corrupted with mean-zero white Gaussian noise. We\nestablish near-oracle performance for recovery of signals sparse in some\narbitrary dictionary. In addition, we analyze the block variant of the method\nfor signals whose supports obey a block structure, extending the method into\nthe model-based compressed sensing framework. Numerical experiments confirm\nthat the block method significantly outperforms the standard method in these\nsettings.\n
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".