Imputation, Social Choice, and Partial Preferences
Authors: John Doucette
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | My initial work on this approach consisted of both a theoretical and experimental component. ... Beyond this exciting theoretical result, I was able to show that the proposed technique is useful in practice, with a detailed empirical evaluation which established the ability of machine learning models to impute missing information from realworld human preferences. ... The correct winner was recovered in more than 90% of runs, and the overall ranking of the candidates matched that obtained by aggregating the ground-truth ballots to a very high degree. Error rates for the scores of individual candidates under the commonly used Borda Count voting rule were typically less than 1%. |
| Researcher Affiliation | Academia | John A. Doucette David R. Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link, an explicit code release statement, or mention of code in supplementary materials. |
| Open Datasets | No | The paper mentions evaluating on 'realworld human preferences' and 'ten realworld elections' but does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'machine learning techniques' and 'classifiers' but does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper describes the general approach but does not contain specific experimental setup details, such as concrete hyperparameter values, training configurations, or system-level settings, in the main text. |