One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees
Bibliographic record
Abstract
Sample-based estimation, which uses a sample to estimate population parameters (e.g., SUM, COUNT, and AVG), has various applications in database systems. A sampler defines how samples are drawn from a population. Various samplers have been proposed (e.g., uniform sampler, stratified sampler, and measure-biased sampler), since there is no single sampler that works well in all cases. To overcome the "one size does not fit all" challenge, we study how to combine multiple samplers to estimate population parameters, and propose SamComb, a novel bandit-based sampler combination framework. Given a set of samplers, a budget, and a population parameter, SamComb can automatically decide how much budget should be allocated to each sampler so that the combined estimation achieves the highest accuracy. We model this sampler combination problem as a multi-armed bandit (MAB) problem and propose effective approaches to balance the exploration and exploitation trade-off in a principled way. We provide theoretical guarantees for our approaches and conduct extensive experiments on both synthetic and real datasets. The results show that there is a strong need to combine multiple samplers, in order to obtain accurate estimations without the knowledge about population predicates and distributions, and SamComb is an effective framework to achieve this goal.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".