Online Prediction of Switching Graph Labelings with Cluster Specialists
Authors: Mark Herbster, James Robinson
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 m.herbster@cs.ucl.ac.uk James Robinson Department of Computer Science University College London London United Kingdom j.robinson@cs.ucl.ac.uk |
| 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. |