Optimal Aggregation of Uncertain Preferences
Authors: Ariel Procaccia, Nisarg Shah
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results in Section 5 indicate that this is true even with preferences from real-world datasets.Specifically, we use five datasets from Preflib (Mattei and Walsh 2013): AGH Course Selection (D1), Netflix (D2), Skate (D3), Sushi (D4), and T-Shirt (D5). We use CPLEX to find the minimum feedback arc set through integer linear programming. |
| Researcher Affiliation | Academia | Ariel D. Procaccia Computer Science Department Carnegie Mellon University arielpro@cs.cmu.edu Nisarg Shah Computer Science Department Carnegie Mellon University nkshah@cs.cmu.edu |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | Specifically, we use five datasets from Preflib (Mattei and Walsh 2013): AGH Course Selection (D1), Netflix (D2), Skate (D3), Sushi (D4), and T-Shirt (D5). |
| Dataset Splits | No | The paper describes a simulation setup where uncertainty is introduced into existing profiles and an approximation ratio is computed over 1000 simulations, but it does not specify traditional training/validation/test dataset splits. |
| Hardware Specification | No | The paper mentions using 'CPLEX' (software) but does not provide any specific details about the hardware used for the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | We use CPLEX to find the minimum feedback arc set through integer linear programming. (No version number for CPLEX is provided). |
| Experiment Setup | Yes | For each preference profile, we compute the approximation ratio averaged over 1000 simulations. In each simulation each vote in the profile is converted into an uncertain vote, represented as the Mallows model whose central ranking is the vote itself and whose noise parameter ϕ is chosen uniformly at random from [0, 1]. |