Computing Parametric Ranking Models via Rank-Breaking
Authors: Hossein Azari Soufiani, David Parkes, Lirong Xia
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are presented to show the computational efficiency along with statistical performance of the proposed method. We conduct experimental studies to compare our algorithm to the MC-EM algorithm for RUMs. We consider RUMs with normal distributions and study running time and Kendall correlation. Experimental results show that our algorithm runs much faster than the MC-EM algorithm while achieving comparable, and sometimes even better Kendall correlation. |
| Researcher Affiliation | Academia | Hossein Azari Soufiani AZARI@FAS.HARVARD.EDU David C. Parkes PARKES@EECS.HARVARD.EDU Harvard University, 33 Oxford Street, Cambridge, MA 02138 USA Lirong Xia XIAL@CS.RPI.EDU Rensselaer Polytechnic Institute, Troy, NY 12180, USA |
| Pseudocode | Yes | Algorithm 1 GMMG(Dr) For all a, a , compute Xa a G (Dr). Compute GMMG(Dr) according to (2) using the moment conditions in (3) (e.g. using gradient descent). return GMMG(Dr). |
| Open Source Code | Yes | The code is provided in the R package Stat Rank (Chen & Azari Soufiani, 2013). We plan to extend the algorithms and analysis to partial orders, non-location families such as RUMs parameterized by mean and variance, and to GRUMs (Azari Soufiani et al., 2013c) and GRUMs with multiple types (Azari Soufiani et al., 2013b). URL http://cran.r-project. org/web/packages/Stat Rank/index.html. |
| Open Datasets | No | The synthetic datasets are generated as follows. Let m = 5. The ground truth γ is generated from the Dirichlet distribution Dirichlet( 1) which is a distribution on an m dimensional unit simplex. Then, for any given γ we generate up to n = 200 full rankings from the location family with normal distributions. This describes how data was generated, but does not provide access information to a public dataset. |
| Dataset Splits | No | The paper mentions generating up to n = 200 full rankings but does not specify any training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | Yes | All experiments are run on a 2.4 Ghz, Intel Core 2 duo 32 bit laptop. |
| Software Dependencies | No | The code is provided in the R package Stat Rank (Chen & Azari Soufiani, 2013). While an R package is mentioned, specific version numbers for R or any dependent libraries are not provided. |
| Experiment Setup | No | The paper describes how synthetic datasets were generated (e.g., m=5, n=200 rankings), but it does not specify concrete experimental setup details such as hyperparameters (learning rates, batch sizes, epochs) for the algorithms used (GMM or MC-EM). |