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..
Learning-Augmented Algorithms for Online Steiner Tree
Authors: Chenyang Xu, Benjamin Moseley8744-8752
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions. [...] This section investigates the empirical performance of the proposed algorithm OAPT and IOAPT for the undirected Steiner tree problem. |
| Researcher Affiliation | Academia | Chenyang Xu 1 College of Computer Science, Zhejiang University EMAIL, Benjamin Moseley 2 Tepper School of Business, Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper describes algorithms step-by-step but does not present them in a formal pseudocode block or algorithm box. |
| Open Source Code | Yes | The code is available at https://github.com/Chenyang-1995/Online-Steiner-Tree |
| Open Datasets | Yes | The road network of Bay Area is provided by The 9th DIMACS Implementation Challenge... We will sample s training instances of k terminals T1, T2, . . . Ts. |
| Dataset Splits | No | The paper describes how training instances are used for a learning algorithm and selection of a parameter, but does not provide specific train/validation/test splits with percentages or sample counts for the main experiment evaluation. |
| Hardware Specification | Yes | The experiments are conducted on a machine running Ubuntu 18.04 with an i7-7800X CPU and 48 GB memory. |
| Software Dependencies | No | The paper mentions the operating system (Ubuntu 18.04) but does not provide specific version numbers for any other software dependencies or libraries used. |
| Experiment Setup | Yes | We only consider θ {0, 0.2, 0.4, 0.6, 0.8, 1}. |