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 [1].
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. |