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..
Online Prediction of Switching Graph Labelings with Cluster Specialists
Authors: Mark Herbster, James Robinson
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on Chicago Divvy Bicycle Sharing data and show that our algorithms significantly outperform an existing algorithm (a kernelized Perceptron) as well as several natural benchmarks. |
| Researcher Affiliation | Academia | Mark Herbster Department of Computer Science University College London London United Kingdom EMAIL James Robinson Department of Computer Science University College London London United Kingdom EMAIL |
| Pseudocode | Yes | In Algorithm 1 we give our switching specialists method. |
| Open Source Code | No | The paper does not provide any statement or link indicating the public availability of the source code for the described methodology. |
| Open Datasets | Yes | Current and historical data is available from the City of Chicago containing a variety of features for each station... https://data.cityofchicago.org/Transportation/Divvy-Bicycle-Stations-Historical/eq45-8inv |
| Dataset Splits | Yes | The first 24 hours of data were used for parameter selection, and the remaining 48 hours of data were used for evaluating performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For every ten-minute snapshot (labeling) we queried 30 vertices uniformly at random (with replacement) in an online fashion, giving a sequence of 8640 trials over 48 hours. We tested ensemble sizes in {1, 3, 5, 9, 17, 33, 65}, using odd numbers to avoid ties. |