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..
Learning Mixtures of Random Utility Models
Authors: Zhibing Zhao, Tristan Villamil, Lirong Xia
AAAI 2018 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |