On Strategyproof Conference Peer Review
Authors: Yichong Xu, Han Zhao, Xiaofei Shi, Nihar B. Shah
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then empirically show that the requisite property on the (authorship) conflict graph is indeed satisfied in the ICLR-17 submissions data, and further demonstrate a simple trick to make the partitioning method more practically appealing under conference peer-review settings. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA 2Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA, USA |
| Pseudocode | Yes | Algorithm 1 Divide-and-Rank assignment, Algorithm 2 Divide-and-Rank aggregation |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper mentions using 'data from the ICLR-17 conference' and 'all papers submitted to ICLR-17 with the given authorship relationship as the conflict graph' but does not provide any concrete access information (e.g., URL, DOI, specific citation to a public dataset repository) for this dataset. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., training, validation, testing percentages or counts) for its analysis of the ICLR-17 data. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for the empirical analysis. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers that would be needed to replicate the experimental analysis. |
| Experiment Setup | No | The empirical part of the paper involves analysis of a graph structure (ICLR-17 data) rather than training a machine learning model, and thus no hyperparameters or system-level training settings are provided. |