Sequential Generalized Likelihood Ratios and Adaptive Treatment Allocation for Optimal Sequential Selection
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Given k ≥ 2 populations from an exponential family, we consider herein the problem of efficient sequential selection of the population with the largest mean subject to a correct selection probability constraint. The selection procedure consists of a sampling rule, a stopping rule, and a terminal decision rule. Efficiency at every parameter configuration is measured by the expected total sampling cost together with the correct selection probability. By using sequential generalized likelihood ratio tests of multiple hypotheses and an adaptive sampling rule based on a constrained optimization problem, we show that it is possible to achieve asymptotic efficiency at the true (but unknown) parameter configuration as the probability of incorrect selection approaches 0, thereby resolving a number of open problems in this area. Finite-sample efficiency of the proposed procedure is demonstrated in simulation studies that also compare the procedure with other sequential selection procedures in the literature.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it