Learning GAI-Decomposable Utility Models for Multiattribute Decision Making
Authors: Margot Herin, Patrice Perny, Nataliya Sokolovska
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical tests are performed to demonstrate the practical efficiency of the learning approach. This section presents the results of numerical tests performed on synthetic and real-world preference data. |
| Researcher Affiliation | Academia | Margot Herin1, Patrice Perny1, Nataliya Sokolovska2 1LIP6, Sorbonne University, Paris 2LCQB, Sorbonne University, Paris |
| Pseudocode | No | The paper contains mathematical formulations and derivations, but it does not include any clearly labeled pseudocode or algorithm blocks describing the proposed method's steps. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use Employee Selection (ESL)... Lecture Evaluation (LEV)... Employee Rejection/Acceptance (ERA)1... Then from the UCI repository, we use CPU and Car MPG (MPG)... Finally, we use the Movehub city ranking2 (CITY) dataset... 1www.openml.org (ESL, LEV and ERA) 2www.kaggle.com/datasets/blitzr/movehub-city-rankings |
| Dataset Splits | Yes | Each dataset is split to produce a training set containing 80% of the examples and a test set with the 20% left. the regularization hyperparameters 𝐶and 𝜆are selected by cross-validation using a number of folds equal to 3. |
| Hardware Specification | Yes | All tests are conducted on a 2.8 GHz Intel Core i7 processor with 16GB RAM and we used the mathematical programming Gurobi solver (version 9.1.2). |
| Software Dependencies | Yes | All tests are conducted on a 2.8 GHz Intel Core i7 processor with 16GB RAM and we used the mathematical programming Gurobi solver (version 9.1.2). |
| Experiment Setup | Yes | We implement our method, called SMKGAI for Sparse Multiple Kernel GAI, with the Gaussian RBF kernel using 𝜎= 1. The tolerance threshold 𝜖is set to 0.01 and the regularization hyperparameters 𝐶and 𝜆are selected by cross-validation using a number of folds equal to 3. |