Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Spectral Ranking using Seriation
Authors: Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
JMLR 2016 | Venue PDF | LLM Run Details | Input Tokens: 31,128 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,562 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now describe numerical experiments using both synthetic and real datasets to compare the performance of Serial Rank with several classical ranking methods. 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 EMAIL C.M.A.P. Ecole Polytechnique Palaiseau, France. Alexandre d Aspremont EMAIL CNRS & DI, UMR 8548 Ecole Normale Sup erieure Paris, France. Milan Vojnovic EMAIL Microsoft Research Cambridge, U.K. |
| Pseudocode | Yes | Algorithm 1 (Serial Rank) Input: A set of pairwise comparisons Ci,j { 1, 0, 1} or [ 1, 1]. 1: Compute a similarity matrix S as in 2.2 2: Compute the Laplacian matrix LS = diag(S1) S (Serial Rank) 3: Compute the Fiedler vector of S. Output: A ranking induced by sorting the Fiedler vector of S (choose either increasing or decreasing order to minimize the number of upsets). |
| Open Source Code | No | The paper does not provide any explicit statement or link for the availability of its source code. |
| Open Datasets | No | The paper mentions using data derived from Top Coder algorithm competitions and England Football Premier League teams, but it does not provide specific access information (links, DOIs, or formal citations) for these datasets as used in their experiments. |
| Dataset Splits | No | The paper does not specify any training, testing, or validation splits for the datasets used in its experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for implementing the described methodology or running experiments. |
| Experiment Setup | No | The paper describes the general setup for numerical experiments, such as using n=100 and averaging over 50 experiments, but it does not provide specific hyperparameters or system-level training settings. |