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
We consider a generalization of stochastic bandits where the set of arms,\n$\\cX$, is allowed to be a generic measurable space and the mean-payoff function\nis "locally Lipschitz" with respect to a dissimilarity function that is known\nto the decision maker. Under this condition we construct an arm selection\npolicy, called HOO (hierarchical optimistic optimization), with improved regret\nbounds compared to previous results for a large class of problems. In\nparticular, our results imply that if $\\cX$ is the unit hypercube in a\nEuclidean space and the mean-payoff function has a finite number of global\nmaxima around which the behavior of the function is locally continuous with a\nknown smoothness degree, then the expected regret of HOO is bounded up to a\nlogarithmic factor by $\\sqrt{n}$, i.e., the rate of growth of the regret is\nindependent of the dimension of the space. We also prove the minimax optimality\nof our algorithm when the dissimilarity is a metric. Our basic strategy has\nquadratic computational complexity as a function of the number of time steps\nand does not rely on the doubling trick. We also introduce a modified strategy,\nwhich relies on the doubling trick but runs in linearithmic time. Both results\nare improvements with respect to previous approaches.\n
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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