Learning and Optimization with Seasonal Patterns
Bibliographic record
Abstract
Decision Making in a Nonstationary Environment with Periodic Rewards Multiarmed bandit (MAB) is a powerful tool in sequential decision making. Traditional MAB models assume constant mean rewards over time, an assumption often too restrictive for real-world applications in which rewards can vary seasonally. In “Learning and Optimization with Seasonal Patterns,” Chen, Wang, and Wang challenge the standard assumption and study a nonstationary MAB model with periodic rewards. They introduce a two-stage policy that combines Fourier analysis with a confidence bound–based learning procedure. This innovative approach allows the algorithm to adapt to time-varying mean rewards that follow a periodic pattern. The first stage estimates the periods of all decision-making arms, whereas the second stage exploits this information to optimize long-term rewards. The study proves that the learning policy is near optimal, achieving a regret upper bound that scales with the square root of the time horizon and the periods of the arms. This work opens new avenues for more adaptive and efficient decision making in many applications that face seasonality, such as the fashion industry and service systems.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".