Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Ranking Wily People Who Rank Each Other
Authors: Anson Kahng, Yasmine Kotturi, Chinmay Kulkarni, David Kurokawa, Ariel Procaccia
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,745 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,667 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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}. |