Ordered racing protocols for automatically configuring algorithms for scaling performance
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
Automated algorithm configuration has been proven to be an effective approach for achieving improved performance of solvers for many computationally hard problems. We consider the challenging situation where the kind of problem instances for which we desire optimised performance is too difficult to be used during the configuration process. Here, we propose a novel combination of racing techniques with existing algorithm configurators to meet this challenge. We demonstrate that, applied to state-of-the-art solver for propositional satisfiability, mixed integer programming and travelling salesman problems, the resulting algorithm configuration protocol achieves better results than previous approaches and in many cases closely matches the bound on performance obtained using an oracle selector. We also report results indicating that the performance of our new racing protocols is quite robust to variations in the confidence level of the test used for eliminating weak configurations, and that performance benefits from presenting instances ordered according to increasing difficulty during the race -- something not done in standard racing procedures.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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