Inductive Pairwise Ranking: Going Beyond thenlog(n) Barrier
Authors: U.N. Niranjan, Arun Rajkumar
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 un.niranjan@uci.edu. Arun Rajkumar Data Analytics Lab Xerox Research Centre India arun.rajkumar@xerox.com. 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. |