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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Do Outliers Ruin Collaboration?
Authors: Mingda Qiao
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,747 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,677 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an algorithm that achieves an O(ηn + ln n) overhead, which is proved to be worst-case optimal. |
| Researcher Affiliation | Academia | 1Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Iterative Robust Collaborative Learning; Algorithm 2 Candidate(G, d, ϵ, δ); Algorithm 3 Test(G, ˆf, ϵ, δ). |
| Open Source Code | No | The paper is theoretical and does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. It does not mention any publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not conduct empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis; it does not include details about an empirical experimental setup, hyperparameters, or training configurations. |