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].
Identifying Outlier Arms in Multi-Armed Bandit
Authors: Honglei Zhuang, Chi Wang, Yifan Wang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on both synthetic and real data show that our solution saves 70-99% of data collection cost from baseline while having nearly perfect accuracy. |
| Researcher Affiliation | Collaboration | Honglei Zhuang1 Chi Wang2 Yifan Wang3 1University of Illinois at Urbana-Champaign 2Microsoft Research, Redmond 3Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Round-Robin Algorithm (RR) and Algorithm 2: Weighted Round-Robin Algorithm (WRR) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a repository. |
| Open Datasets | No | Synthetic. We construct several synthetic datasets... Twitter. We collect a Twitter dataset... No specific links or citations for public access are provided for either dataset used in their experiments. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or detailed splitting methodology for train/validation/test) is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Once an algorithm runs for 107 pulls, the algorithm is forced to terminate and output the current estimated outlier set ˆΩ. We set δ = 0.1. |