Parallelizing Thompson Sampling
Authors: Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 amin.karbasi@yale.edu Vahab Mirrokni Google Research mirrokni@google.com Mohammad Shadravan Yale University mohammad.shadravan@yale.edu |
| 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]. |