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..
Clustering from Labels and Time-Varying Graphs
Authors: Shiau Hong Lim, Yudong Chen, Huan Xu
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical findings are further supported by empirical results on both synthetic and real data. |
| Researcher Affiliation | Academia | Shiau Hong Lim National University of Singapore EMAIL Yudong Chen EECS, University of California, Berkeley EMAIL Huan Xu National University of Singapore EMAIL |
| Pseudocode | No | The paper describes algorithms mathematically and in prose but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | For real data, we use the Reality Mining dataset [26], which contains individuals from two main groups, the MIT Media Lab and the Sloan Business School, which we use as the ground-truth clusters. The dataset records when two individuals interact, i.e., become proximal of each other or make a phone call, over a 9-month period. We choose a window of 14 weeks (the Fall semester) where most individuals have non-empty interaction data. These consist of 85 individuals with 25 of them from Sloan. |
| Dataset Splits | No | The paper mentions 'In each trial, the in/cross-cluster distributions are estimated from a fraction of randomly selected pairwise interaction data,' but does not specify explicit dataset split percentages (e.g., 80/10/10) or absolute sample counts for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using and adapting the ADMM algorithm by [25] but does not specify any software names with version numbers for reproducibility (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper describes general experimental procedures like 'We perform 100 trials' and how data distributions are estimated, but it does not provide specific details on hyperparameters, training configurations, or system-level settings (e.g., learning rate, batch size, optimizer settings). |