Ranking Distributions based on Noisy Sorting
Authors: Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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. |