Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
Authors: Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate this question, both practically (Section 6) and theoretically (Section 7), for several ranking methods (Section 5) and a concrete instantiation of our framework, in which full rankings are drawn from a Plackett-Luce distribution and observations take the form of pairwise preferences (Section 4).To investigate the practical performance of the methods presented in the previous section, we first conducted controlled experiments with synthetic data, for which the ground truth π is known. |
| Researcher Affiliation | Academia | 1Paderborn University, Germany 2University of Oviedo, Spain. |
| Pseudocode | No | The paper describes various algorithms and methods (e.g., in Section 5) using mathematical formulas and descriptive text, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | To compare the methods on a real world data set, we used the Sushi data (Kamishima, 2003) that contains the preferences (full rankings) of 5000 people over 10 types of sushi. |
| Dataset Splits | No | The paper mentions training data and sample sizes, but does not explicitly provide details about validation dataset splits (e.g., percentages, sample counts, or explicit mention of a validation set). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific tool versions) that would be needed for reproducibility. |
| Experiment Setup | No | The paper describes the parameters for generating synthetic data (K, θ, λ) and the number of simulation runs (500), but it does not provide specific experimental setup details such as hyperparameters for the various rank aggregation methods or other training configurations. |