Learning Mixtures of Random Utility Models
Authors: Zhibing Zhao, Tristan Villamil, Lirong Xia
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data show that the sandwich algorithm achieves the highest statistical efficiency and GMM is the most computationally efficient. Experiments on real-world data at Preflib show that Gaussian k-RUMs provide better fitness than a single Gaussian RUM, the Plackett-Luce model, and mixtures of Plackett-Luce models w.r.t. commonly-used model fitness criteria. |
| Researcher Affiliation | Academia | Zhibing Zhao, Tristan Villamil, Lirong Xia Rensselaer Polytechnic Institute 110 8th Street, Troy, NY, USA {zhaoz6, villat2}@rpi.edu, xial@cs.rpi.edu |
| Pseudocode | Yes | Algorithm 1 E-GMM Algorithm Input: Profile P of n rankings, the number of components k, the number of iterations T. Output: α(T +1) r , θ(r,T +1), where r = 1, 2, , k. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | Yes | Experiments on real-world Preflib data (Mattei and Walsh 2013) |
| Dataset Splits | No | The paper does not specify the exact training, validation, and test dataset splits (e.g., percentages, absolute counts, or explicit standard splits). |
| Hardware Specification | Yes | All experiments were run on an Ubuntu Linux server with Intel Xeon E5 v3 CPUs clocked at 3.50 GHz. |
| Software Dependencies | No | The paper states 'We implemented all algorithms with Matlab' but does not provide a specific version number for Matlab or any other key software libraries used with version numbers. |
| Experiment Setup | No | While the paper mentions '10 EM iterations' and '5 EM iterations' in figure captions, it does not provide a comprehensive list of hyperparameters, optimizer settings, or other detailed experimental setup configurations in the main text. |