Controlling the distance to a Kemeny consensus without computing it
Authors: Yunlong Jiao, Anna Korba, Eric Sibony
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, numerical experiments are described in details in Section 6 to address the efficiency and usefulness of our method on real datasets.In this section we study the tightness of the bound in Theorem 1 and the applicability of Method 1 through numerical experiments. |
| Researcher Affiliation | Academia | Yunlong Jiao YUNLONG.JIAO@MINES-PARISTECH.FR MINES Paris Tech CBIO, PSL Research University, Institut Curie, INSERM, U900, Paris, France Anna Korba ANNA.KORBA@TELECOM-PARISTECH.FR LTCI UMR No. 5141 Telecom Paris Tech/CNRS, Institut Mines-Telecom Paris, 75013, France Eric Sibony ERIC.SIBONY@TELECOM-PARISTECH.FR LTCI UMR No. 5141 Telecom Paris Tech/CNRS, Institut Mines-Telecom Paris, 75013, France |
| Pseudocode | Yes | Method 1. Let DN SN n be a dataset and let σ Sn be a permutation considered as an approximation of Kemeny s rule. In practice σ is the consensus returned by a tractable voting rule. 1. Compute kmin(σ; DN) with Formula (5). 2. Then by Theorem 1, d(σ, σ ) kmin(σ; DN) for any Kemeny consenus σ KN. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We report here the results of a case-study on the sushi dataset provided by Kamishima (2003) to illustrate our method.In the 1980 American Psychological Association (APA) presidential election, voters were asked to rank n = 5 candidates in order of preference and a total of N = 5738 complete ballots were reported. With the original collection of ballots introduced by Diaconis (1989), We created 500 bootstrapped pseudosamples following Popova (2012).The effect of datasets DN on the measure s (DN; r, n) is tested with the Netflix data provided by Mattei et al. (2012). |
| Dataset Splits | No | The paper mentions creating 'bootstrapped pseudosamples' and refers to 'test' and 'train' in the context of the Kemeny rule, but it does not provide explicit details on how the datasets were split into training, validation, or test sets for its own experiments (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software with specific version numbers. |
| Experiment Setup | No | The paper describes the numerical experiments by specifying the voting rules and datasets used, but it does not provide specific experimental setup details such as hyperparameters, model initialization, or training schedules. |