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..
Mallows ranking models: maximum likelihood estimate and regeneration
Authors: Wenpin Tang
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we apply Algorithm 1 to provide experimental results on synthetic data and two real-world data: APA election data (large N, small tmax), and University’s homepage search (small N, large tmax). |
| Researcher Affiliation | Academia | 1Department of Mathematics, University of California, Los Angeles, USA. Correspondence to: Wenpin Tang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 t selection algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We consider the problem of ranking with a small number of items and large sample size. The data consists of N = 15449 rankings over tmax = 5 candidates during the American Psychological Association’s presidential election in 1980. ... See (Coombs et al., 1984; Diaconis, 1989) for further background. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., percentages or counts for training, validation, and testing sets). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We generate 50 sets of N = 1000 rankings from the IGM model (1.3) with θ = (1, 0.9, 0.8, 0.7, 0.6, 0.5, 0, . . .) and π0 = id. We restrict all observed rankings to the first tmax = 6 components. |