Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons
Authors: Yuxin Chen, Changho Suh
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The practical applicability of Spectral MLE is further corroborated by numerical experiments. |
| Researcher Affiliation | Academia | Yuxin Chen YXCHEN@STANFORD.EDU Department of Statistics, Stanford University, Stanford, CA 94305, USA Changho Suh CHSUH@KAIST.AC.KR Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Korea |
| Pseudocode | Yes | Algorithm 1 Spectral MLE. |
| Open Source Code | No | The paper does not explicitly state that the source code for its methodology is made publicly available or provide a link to a repository. |
| Open Datasets | No | The latent scores are generated uniformly over [0.5, 1]. For each (pobs, L), the paired comparisons are randomly generated as per the BTL model, and we perform score inference by means of both Rank Centrality and Spectral MLE. The generated data is not made publicly available with access information. |
| Dataset Splits | No | In all numerical simulations performed here, we pick c2 = 5 and c3 = 1, and do not split samples. |
| Hardware Specification | No | The paper describes synthetic experiments but does not provide any specific details about the hardware used to conduct them. |
| Software Dependencies | No | The paper describes algorithms and numerical experiments but does not specify any software libraries or their version numbers used in the implementation. |
| Experiment Setup | Yes | In all numerical simulations performed here, we pick c2 = 5 and c3 = 1, and do not split samples. We focus on the case where n = 100, where each reported result is calculated by averaging over 200 Monte Carlo trials. The latent scores are generated uniformly over [0.5, 1]. For each (pobs, L), the paired comparisons are randomly generated as per the BTL model. |