Assessing the robustness of Multi-Armed Bandit algorithms against biased initialization
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
The robustness of Multi-Armed Bandit (MAB) algorithms forms a cornerstone of the efficacy of contemporary recommender systems. This study provides a comparative analysis of four widely-adopted MAB algorithms—Epsilon Greedy, Explore Then Commit (ETC), Upper Confidence Bound (UCB1), and Thompson Sampling—under the influence of biased initialization. Conducted in a simulated environment that mirrors practical recommender scenarios, the study examines the adaptive responses of these algorithms over time, quantifying their performance using cumulative regret as a primary metric. Our findings indicate varying degrees of resilience, with Epsilon Greedy exhibiting the slowest recovery from initial bias and Thompson Sampling demonstrating consistent adaptability. By exploring the implications of static biases to various multi-armed bandit algorithms, this research contributes foundational insights for advancing the development of robust and equitable recommender systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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