Ranking Wily People Who Rank Each Other

Authors: Anson Kahng, Yasmine Kotturi, Chinmay Kulkarni, David Kurokawa, Ariel Procaccia

AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimental results further support the efficacy and practicability of our algorithms.
Researcher Affiliation Academia Anson Kahng Computer Science Department Carnegie Mellon University Yasmine Kotturi Human-Computer Interaction Institute Carnegie Mellon University Chinmay Kulkarni Human-Computer Interaction Institute Carnegie Mellon University David Kurokawa Computer Science Department Carnegie Mellon University Ariel D. Procaccia Computer Science Department Carnegie Mellon University
Pseudocode Yes Algorithm 1: k-PARTITE, Algorithm 2: NAIVE-BIPARTITE, Algorithm 3: COMMITTEE
Open Source Code No The paper mentions 'Available from http://procaccia.info/research.' which refers to the full version of the paper, but does not explicitly state that the source code for the methodology is provided or link to a code repository.
Open Datasets Yes The input profiles are generated according to the popular Mallows (1957) model. (Mallows, C. L. 1957. Non-null ranking models. Biometrika 44:114–130.)
Dataset Splits No The paper describes how input profiles are generated using the Mallows model and provides parameters (n, φ) for the generation, but it does not specify any train/validation/test dataset splits.
Hardware Specification No The paper mentions running experiments but does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types.
Software Dependencies No The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9).
Experiment Setup Yes Throughout our experiments, we let k = n/4, n/8 for COMMITTEE and let k = 4, 8 for k-PARTITE. We ran experiments with n {8, 16, 24, 32, 40} players and φ {0, 0.1, 0.3, 0.5, 0.7, 0.9, 1}.