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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Do Outliers Ruin Collaboration?
Authors: Mingda Qiao
ICML 2018 | Venue PDF | LLM Run Details
| 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. |