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..
Ranking Distributions based on Noisy Sorting
Authors: Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show that the models perform very well in terms of goodness of fit, compared to existing models for ranking data. |
| Researcher Affiliation | Collaboration | 1Heinz Nixdorf Institute and Department of Computer Science, Paderborn University, Germany 2Yahoo Research, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Metropolis-Hastings with Mallows proposal |
| Open Source Code | No | The paper does not provide any concrete access (e.g., repository link, explicit statement of code release) to the source code for the methodology described. |
| Open Datasets | Yes | To investigate the performance of our new model and the effectiveness of parameter estimation, we conducted experiments on 213 real-world data sets from the Pref Lib repository (http://www.preflib.org). |
| Dataset Splits | Yes | In a first setting, we fit the models to the entire data, while in a second setting, we only fit to half of the data and determine divergence on the other half (averaging over 20 random splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) 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 versions). |
| Experiment Setup | No | The paper describes the general experimental settings, such as fitting models and using K-L divergence, but does not provide specific hyperparameters (e.g., learning rate, batch size, optimizer settings) or system-level training configurations. |