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..
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. |