Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization
Bibliographic record
Abstract
In diverse fields of application, Bayesian Optimization (BO) has been proposed to find the optimum of black-box functions, surpassing human-driven searches. BO’s appeal lies in its data efficiency, making it suitable for optimizing costly-to-evaluate functions without requiring extensive training data. While BO can perform well in closed-loop, domain experts frequently have hypotheses about which parameter combinations are more likely to yield optimal results. Hence, for BO to be truly relevant and adopted by practitioners, such prior knowledge needs to be efficiently and seamlessly integrated into the optimization framework. Some methods were recently developed to address this challenge, but they suffer from robustness issues when provided erroneous insight. Building on the idea of element-wise prior-weighted acquisition function, we propose to use a fixed-weight effective prior that distills expert user knowledge with minimal computational cost. Comprehensive investigation across diverse task conditions and prior quality levels revealed that our method, α - π BO, surpasses Vanilla BO when provided with insights of good quality while maintaining robustness against misleading information. Moreover, unlike other methods, α - π BO typically requires no hyperparameter tuning, largely simplifying its implementation in diverse tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".