Preference Inference through Rescaling Preference Learning
Authors: Nic Wilson, Mojtaba Montazery
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we analyse the new preference relation, deriving results regarding rescale-optimality that entail when scaling makes a difference, and that lead to a characterisation that allows computation of preference. Section 2 defines the preference relation, and the notion of rescale-optimality, and gives some basic properties. It can happen that rescaling makes no difference; we show how to determine this in Section 3. In Sections 4 and 5 we give characterisations for rescale-optimality, that lead to a way of computing the relation, which we test out with benchmarks derived from a real ride-sharing dataset in Section 6. |
| Researcher Affiliation | Academia | Nic Wilson and Mojtaba Montazery Insight Centre for Data Analytics School of Computer Science and IT University College Cork, Ireland {nic.wilson,mojtaba.montazery}@insight-centre.org |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor does it present structured steps formatted like code. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using 'a subset of a year’s worth of real ridesharing records, provided by a commercial ridesharing system Carma (see http://carmacarpool.com/)', but this URL is for a commercial system and not a public dataset repository. No specific citation with authors/year for the dataset is provided. |
| Dataset Splits | No | The paper states, 'We randomly generate 100 pairs of (γ, β), based on a uniform distribution for each feature,' but it does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper states, 'CPLEX 12.6.2 is used as the solver on a computer facilitated by a Core i7 2.60 GHz processor and 8 GB RAM memory.' While it mentions a 'Core i7' processor, it does not specify the exact model number (e.g., Core i7-XXXX) nor any GPU details, which are necessary for full reproducibility. |
| Software Dependencies | Yes | CPLEX 12.6.2 is used as the solver on a computer facilitated by a Core i7 2.60 GHz processor and 8 GB RAM memory. |
| Experiment Setup | No | The paper describes general experimental approach in Section 6, but it does not provide specific details such as hyperparameters, optimization settings, or other system-level training configurations. |