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..
Dominance and Optimisation Based on Scale-Invariant Maximum Margin Preference Learning
Authors: Mojtaba Montazery, Nic Wilson
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we compare the relations and their associated optimality sets based on their decisiveness, computation time and cardinality of the optimal set. We also discuss connections with imprecise probability. |
| Researcher Affiliation | Academia | Mojtaba Montazery and Nic Wilson Insight Centre for Data Analytics School of Computer Science and IT University College Cork, Ireland EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Computational methods are described in textual and mathematical form. |
| Open Source Code | No | The paper does not provide a link to or explicitly state that the source code for its methodology is available. |
| Open Datasets | Yes | The experiments make use of a subset of a year s worth of real ridesharing records. These were provided by a commercial ridesharing system Carma (see http://gocarma.com/). We base our experiments on 13 benchmarks derived from this data set. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It describes how data for generating decisive pairs and optimal solutions was created but not as standard splits. |
| Hardware Specification | Yes | To conduct the experiments, CPLEX 12.6.3 is used as the solver on a computer facilitated by an Intel Xeon E312xx 2.20 GHz processor and 8 GB RAM memory. |
| Software Dependencies | Yes | To conduct the experiments, CPLEX 12.6.3 is used as the solver on a computer facilitated by an Intel Xeon E312xx 2.20 GHz processor and 8 GB RAM memory. |
| Experiment Setup | Yes | Here, we would like to examine how decisive each relation is, i.e., which relation is weaker and by how much. We randomly generate 1000 pairs (α, β), based on a uniform distribution for each feature. A pair (α, β) is called decisive for a preference relation if one of them can (strictly) dominate the other one; for example, the pair (α, β) is decisive for I Λ if and only if α I Λ β or β I Λ α. This is iff either (α I Λ β and β I Λ α) or (β I Λ α and α I Λ β). We also consider another relation I F Λ which is the intersection of I Λ and F Λ, so that α I F Λ β α I Λ β and α F Λ β. |