Bibliographic record
Abstract
We propose a new iterative algorithm for finding a minimum point of f_*:X \subset \mathbb{R}^d \rightarrow \mathbb{R}, when f_* is known to be convex, but only noisy observations of f_*(\textbf{x}) are available at  \textbf{x} \in X for a finite set X. At each iteration of the proposed algorithm, we estimate the probability of each point \textbf{x} \in X being a minimum point of f_* using the fact that f_* is convex, and sample r points from X according  to these probabilities. We then make observations at the sampled points and use these observations to update the probability of each point \textbf{x} \in X  being a minimum point of f_*. Therefore, the proposed algorithm not only estimates the minimum point of f_* but also provides the probability of each point in X being a minimum point of f_*. Numerical results indicate the proposed algorithm converges to a minimum point of f_* as the number of iterations increases and shows fast convergence, especially in the early stage of the iterations.
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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 | 0.006 |
| 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) | 0.001 | 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".