Adversarial Semi-Bandits with Moving Arms
Bibliographic record
Abstract
This paper studies a novel multi-agent combinatorial bandit problem called moving semi-bandits involving$K$agents and$N$arms, extending the problem of semi-bandits with adversar-ial rewards and stochastic arm availabilities (sleeping semi-bandits). The arms move across agents, making each arm available to at most one agent at a time, and the set of available arms for each agent changes over time. In each round, each agent plays up to$m$arms from their own available arm set simultaneously and observes the random loss for each played arm (i.e., semi-bandit feedback). The loss of each arm has no stochastic assumptions, and different agents may generate different random losses for each arm. The primary goal is to minimize the cumulative loss for all agents through collaboration. This bandit problem is motivated by real-world applications, such as traffic scheduling in wireless networks with multiple access points and task assignment for multiple crowdsourcing platforms. To address this challenge, we propose an efficient framework called Moving-FTPL, which guarantees a regret bound of$O(N\sqrt{NTK\mathrm{I}\mathrm{n}T})$over$T$rounds. Moving-Ftplcan reduce the total regret of all K agents by a factor of$\sqrt{K}$compared to scenarios where agents do not collaborate. Additionally, Moving-FTPL takes a step forward for the long-standing problems of a tighter regret bound for sleeping semi-bandits by significantly improving the state-of-the-art regret bound by a factor of$m\sqrt{N}$and imnroving the bound for sleeping adversarial bandits by a factor of$\sqrt{N}$. Furth ermore, we showcase a crowdsourcing application to demonstrate the effectiveness of our proposed algorithm when compared with others.
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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 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".