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..
Recognising Multidimensional Euclidean Preferences
Authors: Dominik Peters
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that, in contrast, for every other fixed dimension d > 1, the recognition problem is equivalent to the existential theory of the reals (ETR), and so in particular NP-hard. We further show that some Euclidean preference profiles require exponentially many bits in order to specify any Euclidean embedding, and prove that the domain of d-Euclidean preferences does not admit a finite forbidden minor characterisation for any d > 1. We also study dichotomous preferences and the behaviour of other metrics, and survey a variety of related work. |
| Researcher Affiliation | Academia | Dominik Peters Department of Computer Science University of Oxford, UK EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide a statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We have run some preliminary experiments on the PrefLib dataset (Mattei and Walsh 2013) using the nlsat solver (Jovanovi c and De Moura 2012) which appears to be the strongest ETR-solver available. |
| Dataset Splits | No | The paper mentions using the PrefLib dataset but does not specify any training, validation, or test splits for it. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the "nlsat solver" but does not specify its version number or any other software dependencies with versions. |
| Experiment Setup | No | The paper mentions attempting experiments with the nlsat solver and a time bound, but does not provide specific experimental setup details like hyperparameters or system-level training settings. |