Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Blocking Bandits
Authors: Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Evaluation: Synthetic Experiments: We first validate our results on synthetic experiments, where we use K = 20 arms. |
| Researcher Affiliation | Collaboration | Soumya Basu Sujay Sanghavi UT Austin, Amazon Sanjay Shakkottai |
| Pseudocode | Yes | Algorithm 1 Upper Confidence Bound Greedy |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code. |
| Open Datasets | Yes | We perform jokes recommendation experiment using the Jesters joke dataset [14]. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions running experiments but does not provide specific hardware details (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The gaps in mean rewards of the arms are fixed with i(i+1), chosen uniformly at random (u.a.r.) from [0.01, 0.05] for all i = 1 to 19. We also fix µK = 0. The rewards are distributed as Bernoulli random variables with mean µi. The delays are fixed either 1) by sampling all delays u.a.r. from [1, 10] (small delay instances), or 2) u.a.r. from [11, 20] (large delay instances), or 3) by fixing all the delay to a single value. ... We rescale the ratings to [0, 1] using x ! (x + 10)/20. |