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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Parallelizing Thompson Sampling
Authors: Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate experimentally that dynamic batch allocation dramatically outperforms natural baselines such as static batch allocations. |
| Researcher Affiliation | Collaboration | Amin Karbasi Yale University EMAIL Vahab Mirrokni Google Research EMAIL Mohammad Shadravan Yale University EMAIL |
| Pseudocode | Yes | Algorithm 1 Batch Thompson Sampling |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. The ethics review section states N/A for code availability. |
| Open Datasets | No | The paper mentions using 'Movie Lens data set' for experiments but does not provide a specific link, DOI, repository name, or a formal citation with author names and year for public access to this dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts for train/validation/test sets, or references to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. The ethics review section also indicates N/A for compute resources. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, used for replicating the experiments. |
| Experiment Setup | Yes | For MOTS, we set ρ = 0.9999 and α = 2 as suggested by Jin et al. [2020]. For the parameters, we set δ = 0.61, σ = 0.01, and ϵ = 0.71 as suggested by Beygelzimer et al. [2011]. |