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.
Learning Preference Models with Sparse Interactions of Criteria
Authors: Margot Herin, Patrice Perny, Nataliya Sokolovska
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,543 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,231 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 | Section 4 presents some numerical tests to evaluate the performance of the proposed approach both in terms of computation time and generalizing performances. |
| Researcher Affiliation | Academia | Margot Herin1 , Patrice Perny 1 , Nataliya Sokolovska2 1Sorbonne University, CNRS, LIP6, Paris, France 2 Sorbonne University, CNRS, LCQB, Paris, France |
| Pseudocode | No | The paper describes algorithmic steps and propositions but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. No repository link or explicit code release statement found. |
| Open Datasets | No | In this section we present the results of numerical tests performed on synthetic preference data. Preference data are generated through randomly drawn sparse M obius vectors m (verifying monotonicity constraints) and utilities vectors x, y are uniformly drawn within [0, 1]n. |
| Dataset Splits | No | We set the size of the training sets to |P| + |I| = 500 and of the test sets to |P| = 1000. No explicit mention of a validation set split. |
| 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 | The regularization parameter λ is set to λ = 1. For the D-IRLS method, the smoothing parameter is set to η = 10 50 and the algorithm terminates when m(k+1) m(k) 2 10 3. Also, coefficients with absolute values smaller than 10 5 are discarded at each iteration. |