Error in the Euclidean Preference Model
Authors: Luke Thorburn, Maria Polukarov, Carmine Ventre
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We extend this result, showing that there are situations in which almost all preference profiles cannot be represented with the Euclidean model, and derive a theoretical lower bound on the expected error when using the Euclidean model to approximate non-Euclidean preference profiles. |
| Researcher Affiliation | Academia | King s College London {luke.thorburn, maria.polukarov, carmine.ventre}@kcl.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, as it is a theoretical paper focused on proving bounds rather than implementing a system. |
| Open Datasets | No | The paper does not describe the use of any specific dataset for training, as it focuses on theoretical analysis and derivations. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for reproducing any data partitioning, as it is a theoretical work. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, memory, etc.) for running experiments, as it is a theoretical paper without empirical experimentation. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment, as it is a theoretical paper. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) as it is a theoretical paper and does not describe empirical experiments. |