{Bayesian Multi-Scale Optimistic Optimization}
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
Bayesian optimization is a powerful global op-timization technique for expensive black-box functions. One of its shortcomings is that it re-quires auxiliary optimization of an acquisition function at each iteration. This auxiliary opti-mization can be costly and very hard to carry out in practice. Moreover, it creates serious theoret-ical concerns, as most of the convergence results assume that the exact optimum of the acquisition function can be found. In this paper, we intro-duce a new technique for efficient global opti-mization that combines Gaussian process confi-dence bounds and treed simultaneous optimistic optimization to eliminate the need for auxiliary optimization of acquisition functions. The exper-iments with global optimization benchmarks and a novel application to automatic information ex-traction demonstrate that the resulting technique is more efficient than the two approaches from which it draws inspiration. Unlike most theo-retical analyses of Bayesian optimization with Gaussian processes, our finite-time convergence rate proofs do not require exact optimization of an acquisition function. That is, our approach eliminates the unsatisfactory assumption that a difficult, potentially NP-hard, problem has to be solved in order to obtain vanishing regret rates. 1
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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