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 [1].
SerialRank: Spectral Ranking using Seriation
Authors: Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real datasets demonstrate that seriation based spectral ranking achieves competitive and in some cases superior performance compared to classical ranking methods. |
| Researcher Affiliation | Collaboration | Fajwel Fogel C.M.A.P., Ecole Polytechnique, Palaiseau, France EMAIL Alexandre d Aspremont CNRS & D.I., Ecole Normale Sup erieure Paris, France EMAIL Milan Vojnovic Microsoft Research, Cambridge, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 Using Seriation for Spectral Ranking (Serial Rank) Input: A set of pairwise comparisons Ci,j 2 { 1, 0, 1} or [ 1, 1]. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using "Synthetic Datasets", "Top Coder algorithm competitions" and "England Football Premier League teams". However, it does not provide concrete access information (e.g., specific links, DOIs, or formal citations for downloadable processed datasets) for these datasets. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, such as percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies used in the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, such as hyperparameter values, optimizer settings, or other training configurations. |