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].
Inductive Pairwise Ranking: Going Beyond thenlog(n) Barrier
Authors: U.N. Niranjan, Arun Rajkumar
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In practice, through systematic synthetic simulations, we confirm our theoretical findings regarding improvements in the sample complexity due to the use of feature information. Moreover, on popular real-world preference learning datasets, with as less as 10% sampling of the pairwise comparisons, our method recovers a good ranking. |
| Researcher Affiliation | Collaboration | U. N. Niranjan Department of Computer Science University of California Irvine EMAIL. Arun Rajkumar Data Analytics Lab Xerox Research Centre India EMAIL. Part of the work done while interning at Xerox Research Centre India. |
| Pseudocode | Yes | Algorithm 3 IPR: Inductive Pairwise Ranking. Subroutine 1 IMC: Inductive Matrix Completion. Subroutine 2 PR: Pairwise Ranking (Copeland Procedure). |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | 1. Sushi: This data (Kamishima and Akaho 2009) is from a survey of 5000 customers. [...] 2. Car: The task in this dataset (Abbasnejad et al. 2013) is find an order of preference among ten cars. |
| Dataset Splits | No | The paper mentions "10% sampling" in the abstract and discussion of varying 'm' (number of pairs compared) for synthetic data, but does not provide explicit train/validation/test splits, percentages, or absolute counts for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For IPR, we choose λL = 10-2 and λN = 102. |