Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression
Authors: Nadjet Bourdache, Patrice Perny, Olivier Spanjaard
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical tests showing the interest of the proposed approach. and 5 Numerical Tests Before coming to the results of numerical tests2 we conducted on randomly generated instances to evaluate the behavior of Algorithm 1 |
| Researcher Affiliation | Academia | Nadjet Bourdache , Patrice Perny and Olivier Spanjaard Sorbonne Universit e, CNRS, LIP6, F-75005 Paris, France |
| Pseudocode | Yes | Algorithm 1 Incremental decision making and Algorithm 2 Approximating density function p(w|y(i)) |
| Open Source Code | No | No explicit statement or link for open-source code specific to the methodology described in this paper was found. |
| Open Datasets | No | We generate instances with 5 criteria and 100 Pareto optimal alternatives. Every alternative a in each generated set A is drawn as follows: a first vector v of size p 1 is uniformly drawn in [0, 1]p 1, then a is obtained by setting ai =v(i) v(i 1) for i=1, . . . , p, where v(0) =0 and v(p) =1. |
| Dataset Splits | No | No explicit mention of training, validation, or test dataset splits was found. |
| Hardware Specification | Yes | running on an Intel(R) Core(TM) i7-4790 CPU with 15GB of RAM. |
| Software Dependencies | No | Implementation in Python using the tmvtnorm R s library to draw vectors according to multivariate truncated normal densities (Specific version numbers for Python or the tmvtnorm library are not provided.) |
| Experiment Setup | Yes | Assume that Algorithm 1 is launched with an acceptance threshold δ = 0.02. For the elicitation of OWA parameters, the prior is set to N((10, . . . , 10), 100I), where I is the identity matrix. For Choquet parameters, the prior is set to N(µ, 100I), where µi =10 if |Yi| = 1 and µi =0 otherwise and To simulate the DM s answers, we use the model given in Equation 4 with ε(i) N(0, σ2) for σ {0, 0.1, 0.2} |