Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Error in the Euclidean Preference Model
Authors: Luke Thorburn, Maria Polukarov, Carmine Ventre
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,319 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,679 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |