Online Learning of Capacity-Based Preference Models
Authors: Margot Herin, Patrice Perny, Nataliya Sokolovska
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical tests using synthetic preference data We generate preference data by randomly drawing sparse (with few non-null coefficients) normalized M obius vector m associated with monotonic capacities and pairs of alternatives xt, yt [0, 1]n. Then, after comparison of the perturbed overall values m, ϕ(xt) + ϵx and m, ϕ(yt) + ϵy (where ϵx is a centered Gaussian noise with standard error σ = 0.03), we obtain preference or indifference examples. |
| Researcher Affiliation | Academia | Margot Herin1 , Patrice Perny 1 , Nataliya Sokolovska2 1Sorbonne University, CNRS, LIP6, Paris, France 2 Sorbonne University, CNRS, LCQB, Paris, France |
| Pseudocode | Yes | Algorithm 1 Parameter: (γ, λ, T) 1: t 1, m1 (0, . . . , 0) 2: while t < T do ... Algorithm 2 Parameter: (γ, λ, ρ, T) 1: t 1, m1, µ1, z1 (0, . . . , 0) 2: while t < T do ... |
| Open Source Code | Yes | The code and the proofs not included in the paper are available at https://gitlab.com/margother/OPL. |
| Open Datasets | No | We generate preference data by randomly drawing sparse (with few non-null coefficients) normalized M obius vector m associated with monotonic capacities and pairs of alternatives xt, yt [0, 1]n. (The paper uses synthetic data generated by the authors, with no indication of public availability.) |
| Dataset Splits | No | The accuracy is computed as the average proportion of correctly predicted preferences within a test set containing 500 preference examples. (The paper mentions a test set size but does not provide specific training/validation/test splits.) |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were provided in the paper. |
| Software Dependencies | No | However, B gets sparser as n increases which allows us to resort to specialized libraries (e.g., scipy.sparse) for efficient matrix products in learning algorithms. (Only a library name without a version number is mentioned, and no other software dependencies with versions are listed.) |
| Experiment Setup | Yes | The L1-regularization parameter λ is set to 0.01 for both methods and for Algorithm 1, γ is set to 103. In Table 1 and 2 we compare the average accuracy and training times over 20 simulations of both methods for a growing number of criteria n. ... hyperparameters λ and γ are unchanged and ρ = 1 for Algorithm 2. |